Overview

Dataset statistics

Number of variables30
Number of observations45426
Missing cells218886
Missing cells (%)16.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.7 MiB
Average record size in memory248.0 B

Variable types

Numeric11
Text17
DateTime1
Categorical1

Alerts

budget is highly overall correlated with revenue and 1 other fieldsHigh correlation
popularity is highly overall correlated with vote_countHigh correlation
revenue is highly overall correlated with budget and 3 other fieldsHigh correlation
vote_count is highly overall correlated with popularity and 1 other fieldsHigh correlation
return is highly overall correlated with budget and 1 other fieldsHigh correlation
ganancia is highly overall correlated with revenueHigh correlation
status is highly imbalanced (97.0%)Imbalance
overview has 951 (2.1%) missing valuesMissing
tagline has 25020 (55.1%) missing valuesMissing
id_collection has 40938 (90.1%) missing valuesMissing
name_collection has 40938 (90.1%) missing valuesMissing
poster_path_collection has 41481 (91.3%) missing valuesMissing
backdrop_path_collection has 42165 (92.8%) missing valuesMissing
name_company has 11853 (26.1%) missing valuesMissing
id_company has 11853 (26.1%) missing valuesMissing
names_cast has 2399 (5.3%) missing valuesMissing
director has 874 (1.9%) missing valuesMissing
popularity is highly skewed (γ1 = 29.22370547)Skewed
return is highly skewed (γ1 = 138.4057244)Skewed
budget has 36537 (80.4%) zerosZeros
revenue has 38021 (83.7%) zerosZeros
runtime has 1552 (3.4%) zerosZeros
vote_average has 2988 (6.6%) zerosZeros
vote_count has 2890 (6.4%) zerosZeros
return has 40045 (88.2%) zerosZeros
ganancia has 34533 (76.0%) zerosZeros

Reproduction

Analysis started2023-06-24 19:30:15.146604
Analysis finished2023-06-24 19:31:07.942142
Duration52.8 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

budget
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1223
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4227998.2
Minimum0
Maximum3.8 × 108
Zeros36537
Zeros (%)80.4%
Negative0
Negative (%)0.0%
Memory size709.8 KiB
2023-06-24T16:31:08.112826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile25000000
Maximum3.8 × 108
Range3.8 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17430815
Coefficient of variation (CV)4.122711
Kurtosis66.709853
Mean4227998.2
Median Absolute Deviation (MAD)0
Skewness7.1223549
Sum1.9206105 × 1011
Variance3.038333 × 1014
MonotonicityNot monotonic
2023-06-24T16:31:08.719721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36537
80.4%
5000000 286
 
0.6%
10000000 259
 
0.6%
20000000 243
 
0.5%
2000000 242
 
0.5%
15000000 226
 
0.5%
3000000 223
 
0.5%
25000000 206
 
0.5%
1000000 197
 
0.4%
30000000 190
 
0.4%
Other values (1213) 6817
 
15.0%
ValueCountFrequency (%)
0 36537
80.4%
1 25
 
0.1%
2 14
 
< 0.1%
3 9
 
< 0.1%
4 8
 
< 0.1%
5 8
 
< 0.1%
6 5
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
380000000 1
 
< 0.1%
300000000 1
 
< 0.1%
280000000 1
 
< 0.1%
270000000 1
 
< 0.1%
260000000 3
 
< 0.1%
258000000 1
 
< 0.1%
255000000 1
 
< 0.1%
250000000 10
< 0.1%
245000000 2
 
< 0.1%
237000000 1
 
< 0.1%

id
Real number (ℝ)

Distinct45412
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108282.98
Minimum2
Maximum469172
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size709.8 KiB
2023-06-24T16:31:09.004556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5398
Q126433.5
median59963.5
Q3157159
95-th percentile358347.75
Maximum469172
Range469170
Interquartile range (IQR)130725.5

Descriptive statistics

Standard deviation112398.46
Coefficient of variation (CV)1.0380068
Kurtosis0.55000834
Mean108282.98
Median Absolute Deviation (MAD)44496.5
Skewness1.2803691
Sum4.9188625 × 109
Variance1.2633414 × 1010
MonotonicityNot monotonic
2023-06-24T16:31:09.268934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110428 2
 
< 0.1%
99080 2
 
< 0.1%
22649 2
 
< 0.1%
14788 2
 
< 0.1%
132641 2
 
< 0.1%
10991 2
 
< 0.1%
12600 2
 
< 0.1%
77221 2
 
< 0.1%
13209 2
 
< 0.1%
15028 2
 
< 0.1%
Other values (45402) 45406
> 99.9%
ValueCountFrequency (%)
2 1
< 0.1%
3 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
ValueCountFrequency (%)
469172 1
< 0.1%
468707 1
< 0.1%
468343 1
< 0.1%
467731 1
< 0.1%
465044 1
< 0.1%
464819 1
< 0.1%
464207 1
< 0.1%
464111 1
< 0.1%
463906 1
< 0.1%
463800 1
< 0.1%
Distinct89
Distinct (%)0.2%
Missing11
Missing (%)< 0.1%
Memory size709.8 KiB
2023-06-24T16:31:09.525794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters90830
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen
ValueCountFrequency (%)
en 32245
71.0%
fr 2437
 
5.4%
it 1529
 
3.4%
ja 1349
 
3.0%
de 1078
 
2.4%
es 993
 
2.2%
ru 824
 
1.8%
hi 508
 
1.1%
ko 444
 
1.0%
zh 409
 
0.9%
Other values (79) 3599
 
7.9%
2023-06-24T16:31:09.998499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 34571
38.1%
n 32954
36.3%
r 3633
 
4.0%
f 2836
 
3.1%
i 2389
 
2.6%
t 2250
 
2.5%
a 1839
 
2.0%
s 1652
 
1.8%
j 1350
 
1.5%
d 1322
 
1.5%
Other values (16) 6034
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 90830
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 34571
38.1%
n 32954
36.3%
r 3633
 
4.0%
f 2836
 
3.1%
i 2389
 
2.6%
t 2250
 
2.5%
a 1839
 
2.0%
s 1652
 
1.8%
j 1350
 
1.5%
d 1322
 
1.5%
Other values (16) 6034
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 90830
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 34571
38.1%
n 32954
36.3%
r 3633
 
4.0%
f 2836
 
3.1%
i 2389
 
2.6%
t 2250
 
2.5%
a 1839
 
2.0%
s 1652
 
1.8%
j 1350
 
1.5%
d 1322
 
1.5%
Other values (16) 6034
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90830
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 34571
38.1%
n 32954
36.3%
r 3633
 
4.0%
f 2836
 
3.1%
i 2389
 
2.6%
t 2250
 
2.5%
a 1839
 
2.0%
s 1652
 
1.8%
j 1350
 
1.5%
d 1322
 
1.5%
Other values (16) 6034
 
6.6%

overview
Text

MISSING 

Distinct44288
Distinct (%)99.6%
Missing951
Missing (%)2.1%
Memory size709.8 KiB
2023-06-24T16:31:10.559166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length1000
Median length785
Mean length323.35013
Min length1

Characters and Unicode

Total characters14380997
Distinct characters429
Distinct categories25 ?
Distinct scripts13 ?
Distinct blocks21 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44243 ?
Unique (%)99.5%

Sample

1st rowLed by Woody, Andy's toys live happily in his room until Andy's birthday brings Buzz Lightyear onto the scene. Afraid of losing his place in Andy's heart, Woody plots against Buzz. But when circumstances separate Buzz and Woody from their owner, the duo eventually learns to put aside their differences.
2nd rowWhen siblings Judy and Peter discover an enchanted board game that opens the door to a magical world, they unwittingly invite Alan -- an adult who's been trapped inside the game for 26 years -- into their living room. Alan's only hope for freedom is to finish the game, which proves risky as all three find themselves running from giant rhinoceroses, evil monkeys and other terrifying creatures.
3rd rowA family wedding reignites the ancient feud between next-door neighbors and fishing buddies John and Max. Meanwhile, a sultry Italian divorcée opens a restaurant at the local bait shop, alarming the locals who worry she'll scare the fish away. But she's less interested in seafood than she is in cooking up a hot time with Max.
4th rowCheated on, mistreated and stepped on, the women are holding their breath, waiting for the elusive "good man" to break a string of less-than-stellar lovers. Friends and confidants Vannah, Bernie, Glo and Robin talk it all out, determined to find a better way to breathe.
5th rowJust when George Banks has recovered from his daughter's wedding, he receives the news that she's pregnant ... and that George's wife, Nina, is expecting too. He was planning on selling their home, but that's a plan that -- like George -- will have to change with the arrival of both a grandchild and a kid of his own.
ValueCountFrequency (%)
the 138266
 
5.6%
a 98968
 
4.0%
and 75354
 
3.1%
to 73393
 
3.0%
of 69673
 
2.8%
in 48185
 
2.0%
is 36534
 
1.5%
his 36176
 
1.5%
with 23911
 
1.0%
her 21497
 
0.9%
Other values (97159) 1829265
74.6%
2023-06-24T16:31:11.460980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2408828
16.8%
e 1365235
 
9.5%
a 941549
 
6.5%
t 935805
 
6.5%
i 852414
 
5.9%
o 830776
 
5.8%
n 823489
 
5.7%
s 768587
 
5.3%
r 745058
 
5.2%
h 601367
 
4.2%
Other values (419) 4107889
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11161870
77.6%
Space Separator 2408866
 
16.8%
Uppercase Letter 391479
 
2.7%
Other Punctuation 313155
 
2.2%
Decimal Number 42305
 
0.3%
Dash Punctuation 36823
 
0.3%
Close Punctuation 10106
 
0.1%
Open Punctuation 10084
 
0.1%
Final Punctuation 4557
 
< 0.1%
Initial Punctuation 883
 
< 0.1%
Other values (15) 869
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1365235
12.2%
a 941549
 
8.4%
t 935805
 
8.4%
i 852414
 
7.6%
o 830776
 
7.4%
n 823489
 
7.4%
s 768587
 
6.9%
r 745058
 
6.7%
h 601367
 
5.4%
l 479368
 
4.3%
Other values (142) 2818222
25.2%
Uppercase Letter
ValueCountFrequency (%)
A 42813
 
10.9%
T 36010
 
9.2%
S 31179
 
8.0%
M 23980
 
6.1%
B 23738
 
6.1%
C 22825
 
5.8%
H 19455
 
5.0%
W 18666
 
4.8%
I 16818
 
4.3%
D 16340
 
4.2%
Other values (77) 139655
35.7%
Other Letter
ValueCountFrequency (%)
6
 
4.8%
6
 
4.8%
5
 
4.0%
4
 
3.2%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
2
 
1.6%
2
 
1.6%
Other values (76) 88
70.4%
Other Punctuation
ValueCountFrequency (%)
, 133589
42.7%
. 124907
39.9%
' 31150
 
9.9%
" 11688
 
3.7%
: 3301
 
1.1%
? 2764
 
0.9%
; 2496
 
0.8%
! 1546
 
0.5%
/ 765
 
0.2%
& 455
 
0.1%
Other values (12) 494
 
0.2%
Nonspacing Mark
ValueCountFrequency (%)
ి 4
12.1%
́ 4
12.1%
3
9.1%
̈ 3
9.1%
3
9.1%
3
9.1%
2
 
6.1%
2
 
6.1%
2
 
6.1%
2
 
6.1%
Other values (4) 5
15.2%
Decimal Number
ValueCountFrequency (%)
1 9763
23.1%
0 8290
19.6%
9 6417
15.2%
2 4262
10.1%
5 2446
 
5.8%
8 2383
 
5.6%
3 2342
 
5.5%
4 2180
 
5.2%
7 2135
 
5.0%
6 2087
 
4.9%
Spacing Mark
ValueCountFrequency (%)
11
40.7%
4
 
14.8%
3
 
11.1%
3
 
11.1%
2
 
7.4%
ि 2
 
7.4%
1
 
3.7%
ி 1
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 35296
95.9%
885
 
2.4%
633
 
1.7%
5
 
< 0.1%
4
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
® 45
70.3%
14
 
21.9%
¦ 2
 
3.1%
° 2
 
3.1%
1
 
1.6%
Math Symbol
ValueCountFrequency (%)
~ 20
46.5%
+ 12
27.9%
= 6
 
14.0%
| 4
 
9.3%
1
 
2.3%
Open Punctuation
ValueCountFrequency (%)
( 10030
99.5%
[ 51
 
0.5%
{ 2
 
< 0.1%
1
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
$ 318
96.4%
£ 10
 
3.0%
1
 
0.3%
1
 
0.3%
Space Separator
ValueCountFrequency (%)
2408828
> 99.9%
  36
 
< 0.1%
  2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 10054
99.5%
] 50
 
0.5%
} 2
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
3848
84.4%
690
 
15.1%
» 19
 
0.4%
Initial Punctuation
ValueCountFrequency (%)
672
76.1%
193
 
21.9%
« 18
 
2.0%
Control
ValueCountFrequency (%)
106
96.4%
’ 3
 
2.7%
 1
 
0.9%
Modifier Symbol
ValueCountFrequency (%)
´ 25
65.8%
` 12
31.6%
¯ 1
 
2.6%
Format
ValueCountFrequency (%)
31
60.8%
­ 20
39.2%
Other Number
ValueCountFrequency (%)
¹ 8
50.0%
½ 8
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 19
100.0%
Line Separator
ValueCountFrequency (%)
7
100.0%
Letter Number
ValueCountFrequency (%)
2
100.0%
Modifier Letter
ValueCountFrequency (%)
ʼ 2
100.0%
Paragraph Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11548117
80.3%
Common 2827461
 
19.7%
Cyrillic 4587
 
< 0.1%
Greek 648
 
< 0.1%
Devanagari 77
 
< 0.1%
Telugu 30
 
< 0.1%
Hiragana 20
 
< 0.1%
Tamil 19
 
< 0.1%
Han 10
 
< 0.1%
Hangul 9
 
< 0.1%
Other values (3) 19
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1365235
11.8%
a 941549
 
8.2%
t 935805
 
8.1%
i 852414
 
7.4%
o 830776
 
7.2%
n 823489
 
7.1%
s 768587
 
6.7%
r 745058
 
6.5%
h 601367
 
5.2%
l 479368
 
4.2%
Other values (132) 3204469
27.7%
Common
ValueCountFrequency (%)
2408828
85.2%
, 133589
 
4.7%
. 124907
 
4.4%
- 35296
 
1.2%
' 31150
 
1.1%
" 11688
 
0.4%
) 10054
 
0.4%
( 10030
 
0.4%
1 9763
 
0.3%
0 8290
 
0.3%
Other values (71) 43866
 
1.6%
Cyrillic
ValueCountFrequency (%)
о 470
 
10.2%
е 404
 
8.8%
а 373
 
8.1%
н 323
 
7.0%
и 299
 
6.5%
т 265
 
5.8%
р 240
 
5.2%
с 218
 
4.8%
в 173
 
3.8%
л 161
 
3.5%
Other values (46) 1661
36.2%
Greek
ValueCountFrequency (%)
α 60
 
9.3%
ο 55
 
8.5%
τ 43
 
6.6%
η 36
 
5.6%
ι 36
 
5.6%
ν 34
 
5.2%
ε 31
 
4.8%
ρ 31
 
4.8%
π 30
 
4.6%
ς 30
 
4.6%
Other values (33) 262
40.4%
Devanagari
ValueCountFrequency (%)
11
 
14.3%
6
 
7.8%
6
 
7.8%
5
 
6.5%
4
 
5.2%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
Other values (21) 30
39.0%
Hiragana
ValueCountFrequency (%)
4
20.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (7) 7
35.0%
Telugu
ValueCountFrequency (%)
ి 4
13.3%
3
10.0%
3
10.0%
3
10.0%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
1
 
3.3%
Other values (6) 6
20.0%
Tamil
ValueCountFrequency (%)
3
15.8%
2
10.5%
2
10.5%
2
10.5%
2
10.5%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (3) 3
15.8%
Han
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
Hangul
ValueCountFrequency (%)
2
22.2%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Thai
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Arabic
ValueCountFrequency (%)
م 2
50.0%
ہ 1
25.0%
ت 1
25.0%
Inherited
ValueCountFrequency (%)
́ 4
57.1%
̈ 3
42.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14362991
99.9%
Punctuation 7277
 
0.1%
None 5931
 
< 0.1%
Cyrillic 4587
 
< 0.1%
Devanagari 77
 
< 0.1%
Telugu 30
 
< 0.1%
Hiragana 20
 
< 0.1%
Tamil 19
 
< 0.1%
Letterlike Symbols 14
 
< 0.1%
CJK 10
 
< 0.1%
Other values (11) 41
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2408828
16.8%
e 1365235
 
9.5%
a 941549
 
6.6%
t 935805
 
6.5%
i 852414
 
5.9%
o 830776
 
5.8%
n 823489
 
5.7%
s 768587
 
5.4%
r 745058
 
5.2%
h 601367
 
4.2%
Other values (82) 4089883
28.5%
Punctuation
ValueCountFrequency (%)
3848
52.9%
885
 
12.2%
690
 
9.5%
672
 
9.2%
633
 
8.7%
304
 
4.2%
193
 
2.7%
31
 
0.4%
7
 
0.1%
5
 
0.1%
Other values (4) 9
 
0.1%
None
ValueCountFrequency (%)
é 1550
26.1%
ä 294
 
5.0%
á 293
 
4.9%
ö 250
 
4.2%
í 244
 
4.1%
è 209
 
3.5%
ü 178
 
3.0%
ı 165
 
2.8%
ó 164
 
2.8%
ç 158
 
2.7%
Other values (141) 2426
40.9%
Cyrillic
ValueCountFrequency (%)
о 470
 
10.2%
е 404
 
8.8%
а 373
 
8.1%
н 323
 
7.0%
и 299
 
6.5%
т 265
 
5.8%
р 240
 
5.2%
с 218
 
4.8%
в 173
 
3.8%
л 161
 
3.5%
Other values (46) 1661
36.2%
Letterlike Symbols
ValueCountFrequency (%)
14
100.0%
Devanagari
ValueCountFrequency (%)
11
 
14.3%
6
 
7.8%
6
 
7.8%
5
 
6.5%
4
 
5.2%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
Other values (21) 30
39.0%
Hiragana
ValueCountFrequency (%)
4
20.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (7) 7
35.0%
Telugu
ValueCountFrequency (%)
ి 4
13.3%
3
10.0%
3
10.0%
3
10.0%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
1
 
3.3%
Other values (6) 6
20.0%
Alphabetic PF
ValueCountFrequency (%)
4
100.0%
Diacriticals
ValueCountFrequency (%)
́ 4
57.1%
̈ 3
42.9%
Tamil
ValueCountFrequency (%)
3
15.8%
2
10.5%
2
10.5%
2
10.5%
2
10.5%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (3) 3
15.8%
Hangul
ValueCountFrequency (%)
2
22.2%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Number Forms
ValueCountFrequency (%)
2
100.0%
Modifier Letters
ValueCountFrequency (%)
ʼ 2
100.0%
Thai
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Arabic
ValueCountFrequency (%)
م 2
50.0%
ہ 1
25.0%
ت 1
25.0%
CJK
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
Math Operators
ValueCountFrequency (%)
1
100.0%
Katakana
ValueCountFrequency (%)
1
100.0%
Currency Symbols
ValueCountFrequency (%)
1
50.0%
1
50.0%
Specials
ValueCountFrequency (%)
1
100.0%

popularity
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct43746
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9225405
Minimum0
Maximum547.4883
Zeros63
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size709.8 KiB
2023-06-24T16:31:11.738820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01941275
Q10.38646025
median1.128248
Q33.6811312
95-th percentile11.061808
Maximum547.4883
Range547.4883
Interquartile range (IQR)3.294671

Descriptive statistics

Standard deviation6.0069907
Coefficient of variation (CV)2.0554003
Kurtosis1925.0817
Mean2.9225405
Median Absolute Deviation (MAD)0.9674575
Skewness29.223705
Sum132759.32
Variance36.083937
MonotonicityNot monotonic
2023-06-24T16:31:11.977682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 63
 
0.1%
1 × 10-656
 
0.1%
0.000308 43
 
0.1%
0.00022 40
 
0.1%
0.000578 38
 
0.1%
0.000844 38
 
0.1%
0.001177 38
 
0.1%
0.002001 28
 
0.1%
0.003013 21
 
< 0.1%
0.00353 19
 
< 0.1%
Other values (43736) 45042
99.2%
ValueCountFrequency (%)
0 63
0.1%
1 × 10-656
0.1%
2 × 10-66
 
< 0.1%
3 × 10-66
 
< 0.1%
4 × 10-65
 
< 0.1%
5 × 10-61
 
< 0.1%
6 × 10-64
 
< 0.1%
7 × 10-61
 
< 0.1%
8 × 10-66
 
< 0.1%
9 × 10-62
 
< 0.1%
ValueCountFrequency (%)
547.488298 1
< 0.1%
294.337037 1
< 0.1%
287.253654 1
< 0.1%
228.032744 1
< 0.1%
213.849907 1
< 0.1%
187.860492 1
< 0.1%
185.330992 1
< 0.1%
185.070892 1
< 0.1%
183.870374 1
< 0.1%
154.801009 1
< 0.1%
Distinct17333
Distinct (%)38.2%
Missing67
Missing (%)0.1%
Memory size709.8 KiB
Minimum1874-12-09 00:00:00
Maximum2020-12-16 00:00:00
2023-06-24T16:31:12.227541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:12.497388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

revenue
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6863
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11217698
Minimum0
Maximum2.7879651 × 109
Zeros38021
Zeros (%)83.7%
Negative0
Negative (%)0.0%
Memory size709.8 KiB
2023-06-24T16:31:12.744242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile47961745
Maximum2.7879651 × 109
Range2.7879651 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation64355594
Coefficient of variation (CV)5.7369699
Kurtosis237.33518
Mean11217698
Median Absolute Deviation (MAD)0
Skewness12.261424
Sum5.0957513 × 1011
Variance4.1416424 × 1015
MonotonicityNot monotonic
2023-06-24T16:31:12.990548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38021
83.7%
12000000 20
 
< 0.1%
10000000 19
 
< 0.1%
11000000 19
 
< 0.1%
2000000 18
 
< 0.1%
6000000 17
 
< 0.1%
5000000 14
 
< 0.1%
8000000 13
 
< 0.1%
500000 13
 
< 0.1%
1 12
 
< 0.1%
Other values (6853) 7260
 
16.0%
ValueCountFrequency (%)
0 38021
83.7%
1 12
 
< 0.1%
2 3
 
< 0.1%
3 9
 
< 0.1%
4 4
 
< 0.1%
5 5
 
< 0.1%
6 2
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
2787965087 1
< 0.1%
2068223624 1
< 0.1%
1845034188 1
< 0.1%
1519557910 1
< 0.1%
1513528810 1
< 0.1%
1506249360 1
< 0.1%
1405403694 1
< 0.1%
1342000000 1
< 0.1%
1274219009 1
< 0.1%
1262886337 1
< 0.1%

runtime
Real number (ℝ)

ZEROS 

Distinct353
Distinct (%)0.8%
Missing255
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean94.135065
Minimum0
Maximum1256
Zeros1552
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size709.8 KiB
2023-06-24T16:31:13.253750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q185
median95
Q3107
95-th percentile138
Maximum1256
Range1256
Interquartile range (IQR)22

Descriptive statistics

Standard deviation38.377211
Coefficient of variation (CV)0.40768242
Kurtosis93.510501
Mean94.135065
Median Absolute Deviation (MAD)11
Skewness4.4715684
Sum4252175
Variance1472.8103
MonotonicityNot monotonic
2023-06-24T16:31:13.523735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 2555
 
5.6%
0 1552
 
3.4%
100 1470
 
3.2%
95 1411
 
3.1%
93 1214
 
2.7%
96 1104
 
2.4%
92 1079
 
2.4%
94 1062
 
2.3%
91 1056
 
2.3%
88 1032
 
2.3%
Other values (343) 31636
69.6%
ValueCountFrequency (%)
0 1552
3.4%
1 107
 
0.2%
2 33
 
0.1%
3 48
 
0.1%
4 51
 
0.1%
5 51
 
0.1%
6 72
 
0.2%
7 103
 
0.2%
8 78
 
0.2%
9 63
 
0.1%
ValueCountFrequency (%)
1256 1
< 0.1%
1140 2
< 0.1%
931 1
< 0.1%
925 1
< 0.1%
900 1
< 0.1%
877 1
< 0.1%
874 1
< 0.1%
840 2
< 0.1%
780 1
< 0.1%
720 1
< 0.1%

status
Categorical

IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing81
Missing (%)0.2%
Memory size709.8 KiB
Released
44982 
Rumored
 
229
Post Production
 
98
In Production
 
20
Planned
 
15

Length

Max length15
Median length8
Mean length8.0119528
Min length7

Characters and Unicode

Total characters363302
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowReleased
2nd rowReleased
3rd rowReleased
4th rowReleased
5th rowReleased

Common Values

ValueCountFrequency (%)
Released 44982
99.0%
Rumored 229
 
0.5%
Post Production 98
 
0.2%
In Production 20
 
< 0.1%
Planned 15
 
< 0.1%
Canceled 1
 
< 0.1%
(Missing) 81
 
0.2%

Length

2023-06-24T16:31:13.779572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-24T16:31:14.023448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
released 44982
98.9%
rumored 229
 
0.5%
production 118
 
0.3%
post 98
 
0.2%
in 20
 
< 0.1%
planned 15
 
< 0.1%
canceled 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 135192
37.2%
d 45345
 
12.5%
R 45211
 
12.4%
s 45080
 
12.4%
l 44998
 
12.4%
a 44998
 
12.4%
o 563
 
0.2%
r 347
 
0.1%
u 347
 
0.1%
P 231
 
0.1%
Other values (8) 990
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 317721
87.5%
Uppercase Letter 45463
 
12.5%
Space Separator 118
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 135192
42.6%
d 45345
 
14.3%
s 45080
 
14.2%
l 44998
 
14.2%
a 44998
 
14.2%
o 563
 
0.2%
r 347
 
0.1%
u 347
 
0.1%
m 229
 
0.1%
t 216
 
0.1%
Other values (3) 406
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
R 45211
99.4%
P 231
 
0.5%
I 20
 
< 0.1%
C 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
118
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 363184
> 99.9%
Common 118
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 135192
37.2%
d 45345
 
12.5%
R 45211
 
12.4%
s 45080
 
12.4%
l 44998
 
12.4%
a 44998
 
12.4%
o 563
 
0.2%
r 347
 
0.1%
u 347
 
0.1%
P 231
 
0.1%
Other values (7) 872
 
0.2%
Common
ValueCountFrequency (%)
118
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 363302
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 135192
37.2%
d 45345
 
12.5%
R 45211
 
12.4%
s 45080
 
12.4%
l 44998
 
12.4%
a 44998
 
12.4%
o 563
 
0.2%
r 347
 
0.1%
u 347
 
0.1%
P 231
 
0.1%
Other values (8) 990
 
0.3%

tagline
Text

MISSING 

Distinct20281
Distinct (%)99.4%
Missing25020
Missing (%)55.1%
Memory size709.8 KiB
2023-06-24T16:31:14.496408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length297
Median length204
Mean length47
Min length1

Characters and Unicode

Total characters959082
Distinct characters170
Distinct categories17 ?
Distinct scripts6 ?
Distinct blocks10 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20178 ?
Unique (%)98.9%

Sample

1st rowRoll the dice and unleash the excitement!
2nd rowStill Yelling. Still Fighting. Still Ready for Love.
3rd rowFriends are the people who let you be yourself... and never let you forget it.
4th rowJust When His World Is Back To Normal... He's In For The Surprise Of His Life!
5th rowA Los Angeles Crime Saga
ValueCountFrequency (%)
the 10997
 
6.3%
a 6816
 
3.9%
of 4403
 
2.5%
to 3584
 
2.1%
is 2796
 
1.6%
in 2693
 
1.5%
and 2686
 
1.5%
you 2389
 
1.4%
1583
 
0.9%
for 1524
 
0.9%
Other values (15106) 134524
77.3%
2023-06-24T16:31:15.337102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
153737
16.0%
e 94446
 
9.8%
t 57284
 
6.0%
o 56589
 
5.9%
a 51507
 
5.4%
n 47528
 
5.0%
i 46063
 
4.8%
r 45002
 
4.7%
s 42379
 
4.4%
h 37176
 
3.9%
Other values (160) 327371
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 680775
71.0%
Space Separator 153737
 
16.0%
Uppercase Letter 75019
 
7.8%
Other Punctuation 44598
 
4.7%
Decimal Number 2687
 
0.3%
Dash Punctuation 1946
 
0.2%
Final Punctuation 98
 
< 0.1%
Open Punctuation 56
 
< 0.1%
Close Punctuation 55
 
< 0.1%
Currency Symbol 37
 
< 0.1%
Other values (7) 74
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 94446
13.9%
t 57284
 
8.4%
o 56589
 
8.3%
a 51507
 
7.6%
n 47528
 
7.0%
i 46063
 
6.8%
r 45002
 
6.6%
s 42379
 
6.2%
h 37176
 
5.5%
l 30192
 
4.4%
Other values (43) 172609
25.4%
Other Letter
ValueCountFrequency (%)
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (24) 24
70.6%
Uppercase Letter
ValueCountFrequency (%)
T 10011
 
13.3%
A 6877
 
9.2%
S 5652
 
7.5%
H 4404
 
5.9%
I 4387
 
5.8%
E 4306
 
5.7%
W 3681
 
4.9%
O 3479
 
4.6%
N 3196
 
4.3%
L 3196
 
4.3%
Other values (20) 25830
34.4%
Other Punctuation
ValueCountFrequency (%)
. 26653
59.8%
! 5785
 
13.0%
' 5676
 
12.7%
, 4229
 
9.5%
? 1159
 
2.6%
" 582
 
1.3%
148
 
0.3%
: 138
 
0.3%
& 84
 
0.2%
* 42
 
0.1%
Other values (7) 102
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 802
29.8%
1 516
19.2%
2 299
 
11.1%
3 208
 
7.7%
9 208
 
7.7%
5 168
 
6.3%
4 140
 
5.2%
7 121
 
4.5%
6 121
 
4.5%
8 104
 
3.9%
Math Symbol
ValueCountFrequency (%)
+ 5
35.7%
= 5
35.7%
| 2
 
14.3%
~ 1
 
7.1%
1
 
7.1%
Dash Punctuation
ValueCountFrequency (%)
- 1929
99.1%
9
 
0.5%
8
 
0.4%
Final Punctuation
ValueCountFrequency (%)
82
83.7%
15
 
15.3%
» 1
 
1.0%
Initial Punctuation
ValueCountFrequency (%)
14
73.7%
4
 
21.1%
« 1
 
5.3%
Open Punctuation
ValueCountFrequency (%)
( 49
87.5%
[ 7
 
12.5%
Close Punctuation
ValueCountFrequency (%)
) 48
87.3%
] 7
 
12.7%
Other Number
ValueCountFrequency (%)
½ 2
66.7%
² 1
33.3%
Modifier Letter
ValueCountFrequency (%)
ˌ 1
50.0%
ˈ 1
50.0%
Space Separator
ValueCountFrequency (%)
153737
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 37
100.0%
Nonspacing Mark
ValueCountFrequency (%)
1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 755794
78.8%
Common 203253
 
21.2%
Han 21
 
< 0.1%
Tamil 5
 
< 0.1%
Hiragana 5
 
< 0.1%
Katakana 4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 94446
 
12.5%
t 57284
 
7.6%
o 56589
 
7.5%
a 51507
 
6.8%
n 47528
 
6.3%
i 46063
 
6.1%
r 45002
 
6.0%
s 42379
 
5.6%
h 37176
 
4.9%
l 30192
 
4.0%
Other values (73) 247628
32.8%
Common
ValueCountFrequency (%)
153737
75.6%
. 26653
 
13.1%
! 5785
 
2.8%
' 5676
 
2.8%
, 4229
 
2.1%
- 1929
 
0.9%
? 1159
 
0.6%
0 802
 
0.4%
" 582
 
0.3%
1 516
 
0.3%
Other values (42) 2185
 
1.1%
Han
ValueCountFrequency (%)
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
Other values (11) 11
52.4%
Tamil
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Hiragana
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Katakana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 958652
> 99.9%
Punctuation 280
 
< 0.1%
None 110
 
< 0.1%
CJK 21
 
< 0.1%
Tamil 5
 
< 0.1%
Hiragana 5
 
< 0.1%
Katakana 4
 
< 0.1%
IPA Ext 2
 
< 0.1%
Modifier Letters 2
 
< 0.1%
Math Operators 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
153737
16.0%
e 94446
 
9.9%
t 57284
 
6.0%
o 56589
 
5.9%
a 51507
 
5.4%
n 47528
 
5.0%
i 46063
 
4.8%
r 45002
 
4.7%
s 42379
 
4.4%
h 37176
 
3.9%
Other values (78) 326941
34.1%
Punctuation
ValueCountFrequency (%)
148
52.9%
82
29.3%
15
 
5.4%
14
 
5.0%
9
 
3.2%
8
 
2.9%
4
 
1.4%
None
ValueCountFrequency (%)
é 18
16.4%
ä 16
14.5%
ö 8
 
7.3%
á 6
 
5.5%
ó 6
 
5.5%
ü 5
 
4.5%
í 5
 
4.5%
ı 5
 
4.5%
· 4
 
3.6%
ć 3
 
2.7%
Other values (26) 34
30.9%
IPA Ext
ValueCountFrequency (%)
ə 2
100.0%
CJK
ValueCountFrequency (%)
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
Other values (11) 11
52.4%
Tamil
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Katakana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Hiragana
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Modifier Letters
ValueCountFrequency (%)
ˌ 1
50.0%
ˈ 1
50.0%
Math Operators
ValueCountFrequency (%)
1
100.0%

title
Text

Distinct42259
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size709.8 KiB
2023-06-24T16:31:15.877771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length105
Median length79
Mean length16.708075
Min length1

Characters and Unicode

Total characters758981
Distinct characters287
Distinct categories17 ?
Distinct scripts7 ?
Distinct blocks12 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39939 ?
Unique (%)87.9%

Sample

1st rowToy Story
2nd rowJumanji
3rd rowGrumpier Old Men
4th rowWaiting to Exhale
5th rowFather of the Bride Part II
ValueCountFrequency (%)
the 14563
 
10.7%
of 4933
 
3.6%
a 2244
 
1.6%
in 1695
 
1.2%
and 1633
 
1.2%
to 1055
 
0.8%
762
 
0.6%
man 665
 
0.5%
love 664
 
0.5%
for 602
 
0.4%
Other values (24414) 107549
78.9%
2023-06-24T16:31:16.725947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
90961
 
12.0%
e 76344
 
10.1%
a 49014
 
6.5%
o 45731
 
6.0%
n 40894
 
5.4%
r 40061
 
5.3%
i 39834
 
5.2%
t 36775
 
4.8%
s 29572
 
3.9%
h 28545
 
3.8%
Other values (277) 281250
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 534940
70.5%
Uppercase Letter 117415
 
15.5%
Space Separator 90961
 
12.0%
Other Punctuation 10508
 
1.4%
Decimal Number 3858
 
0.5%
Dash Punctuation 985
 
0.1%
Close Punctuation 87
 
< 0.1%
Open Punctuation 85
 
< 0.1%
Final Punctuation 38
 
< 0.1%
Other Letter 25
 
< 0.1%
Other values (7) 79
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 76344
14.3%
a 49014
9.2%
o 45731
 
8.5%
n 40894
 
7.6%
r 40061
 
7.5%
i 39834
 
7.4%
t 36775
 
6.9%
s 29572
 
5.5%
h 28545
 
5.3%
l 25973
 
4.9%
Other values (121) 122197
22.8%
Uppercase Letter
ValueCountFrequency (%)
T 16029
13.7%
S 10345
 
8.8%
M 8037
 
6.8%
B 7669
 
6.5%
C 7170
 
6.1%
A 6806
 
5.8%
D 6350
 
5.4%
L 5878
 
5.0%
H 5181
 
4.4%
W 5168
 
4.4%
Other values (65) 38782
33.0%
Other Letter
ValueCountFrequency (%)
ک 2
 
8.0%
ی 2
 
8.0%
ه 2
 
8.0%
چ 2
 
8.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
ا 1
 
4.0%
Other values (11) 11
44.0%
Other Punctuation
ValueCountFrequency (%)
: 3725
35.4%
' 2511
23.9%
. 1603
15.3%
, 1135
 
10.8%
! 648
 
6.2%
& 460
 
4.4%
? 269
 
2.6%
/ 80
 
0.8%
* 19
 
0.2%
# 13
 
0.1%
Other values (8) 45
 
0.4%
Decimal Number
ValueCountFrequency (%)
2 863
22.4%
1 698
18.1%
0 618
16.0%
3 483
12.5%
9 230
 
6.0%
4 229
 
5.9%
5 225
 
5.8%
7 195
 
5.1%
8 161
 
4.2%
6 156
 
4.0%
Math Symbol
ValueCountFrequency (%)
+ 17
70.8%
× 3
 
12.5%
= 1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
Other Number
ValueCountFrequency (%)
½ 12
63.2%
² 3
 
15.8%
³ 2
 
10.5%
1
 
5.3%
1
 
5.3%
Other Symbol
ValueCountFrequency (%)
° 3
37.5%
2
25.0%
1
 
12.5%
1
 
12.5%
1
 
12.5%
Currency Symbol
ValueCountFrequency (%)
$ 18
85.7%
¢ 2
 
9.5%
£ 1
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 970
98.5%
15
 
1.5%
Close Punctuation
ValueCountFrequency (%)
) 82
94.3%
] 5
 
5.7%
Open Punctuation
ValueCountFrequency (%)
( 80
94.1%
[ 5
 
5.9%
Final Punctuation
ValueCountFrequency (%)
37
97.4%
1
 
2.6%
Initial Punctuation
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
90961
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Format
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 651840
85.9%
Common 106601
 
14.0%
Cyrillic 346
 
< 0.1%
Greek 170
 
< 0.1%
Arabic 11
 
< 0.1%
Katakana 8
 
< 0.1%
Han 5
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 76344
 
11.7%
a 49014
 
7.5%
o 45731
 
7.0%
n 40894
 
6.3%
r 40061
 
6.1%
i 39834
 
6.1%
t 36775
 
5.6%
s 29572
 
4.5%
h 28545
 
4.4%
l 25973
 
4.0%
Other values (107) 239097
36.7%
Common
ValueCountFrequency (%)
90961
85.3%
: 3725
 
3.5%
' 2511
 
2.4%
. 1603
 
1.5%
, 1135
 
1.1%
- 970
 
0.9%
2 863
 
0.8%
1 698
 
0.7%
! 648
 
0.6%
0 618
 
0.6%
Other values (50) 2869
 
2.7%
Cyrillic
ValueCountFrequency (%)
о 32
 
9.2%
е 32
 
9.2%
а 29
 
8.4%
н 24
 
6.9%
и 23
 
6.6%
р 22
 
6.4%
к 17
 
4.9%
с 15
 
4.3%
л 14
 
4.0%
т 14
 
4.0%
Other values (38) 124
35.8%
Greek
ValueCountFrequency (%)
α 20
 
11.8%
ο 14
 
8.2%
ι 14
 
8.2%
τ 9
 
5.3%
ρ 8
 
4.7%
ά 8
 
4.7%
λ 8
 
4.7%
ν 7
 
4.1%
ε 6
 
3.5%
ς 6
 
3.5%
Other values (32) 70
41.2%
Katakana
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Arabic
ValueCountFrequency (%)
ک 2
18.2%
ی 2
18.2%
ه 2
18.2%
چ 2
18.2%
ا 1
9.1%
س 1
9.1%
ج 1
9.1%
Han
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 757410
99.8%
None 1130
 
0.1%
Cyrillic 346
 
< 0.1%
Punctuation 62
 
< 0.1%
Arabic 11
 
< 0.1%
Katakana 8
 
< 0.1%
CJK 5
 
< 0.1%
Misc Symbols 3
 
< 0.1%
Math Operators 2
 
< 0.1%
Letterlike Symbols 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
90961
 
12.0%
e 76344
 
10.1%
a 49014
 
6.5%
o 45731
 
6.0%
n 40894
 
5.4%
r 40061
 
5.3%
i 39834
 
5.3%
t 36775
 
4.9%
s 29572
 
3.9%
h 28545
 
3.8%
Other values (76) 279679
36.9%
None
ValueCountFrequency (%)
é 218
19.3%
ä 128
 
11.3%
ö 56
 
5.0%
è 54
 
4.8%
ô 44
 
3.9%
ü 39
 
3.5%
ó 37
 
3.3%
ı 35
 
3.1%
á 35
 
3.1%
í 33
 
2.9%
Other values (108) 451
39.9%
Punctuation
ValueCountFrequency (%)
37
59.7%
15
24.2%
5
 
8.1%
2
 
3.2%
1
 
1.6%
1
 
1.6%
1
 
1.6%
Cyrillic
ValueCountFrequency (%)
о 32
 
9.2%
е 32
 
9.2%
а 29
 
8.4%
н 24
 
6.9%
и 23
 
6.6%
р 22
 
6.4%
к 17
 
4.9%
с 15
 
4.3%
л 14
 
4.0%
т 14
 
4.0%
Other values (38) 124
35.8%
Misc Symbols
ValueCountFrequency (%)
2
66.7%
1
33.3%
Arabic
ValueCountFrequency (%)
ک 2
18.2%
ی 2
18.2%
ه 2
18.2%
چ 2
18.2%
ا 1
9.1%
س 1
9.1%
ج 1
9.1%
CJK
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Number Forms
ValueCountFrequency (%)
1
100.0%
Math Operators
ValueCountFrequency (%)
1
50.0%
1
50.0%
Letterlike Symbols
ValueCountFrequency (%)
1
50.0%
1
50.0%
Katakana
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Arrows
ValueCountFrequency (%)
1
100.0%

vote_average
Real number (ℝ)

ZEROS 

Distinct92
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6192247
Minimum0
Maximum10
Zeros2988
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size709.8 KiB
2023-06-24T16:31:17.017754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median6
Q36.8
95-th percentile7.8
Maximum10
Range10
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.9227586
Coefficient of variation (CV)0.34217508
Kurtosis2.5073534
Mean5.6192247
Median Absolute Deviation (MAD)0.9
Skewness-1.5195901
Sum255258.9
Variance3.6970008
MonotonicityNot monotonic
2023-06-24T16:31:17.285583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2988
 
6.6%
6 2466
 
5.4%
5 1998
 
4.4%
7 1884
 
4.1%
6.5 1722
 
3.8%
6.3 1602
 
3.5%
5.5 1381
 
3.0%
5.8 1369
 
3.0%
6.4 1349
 
3.0%
6.7 1340
 
2.9%
Other values (82) 27327
60.2%
ValueCountFrequency (%)
0 2988
6.6%
0.5 13
 
< 0.1%
0.7 1
 
< 0.1%
1 104
 
0.2%
1.1 1
 
< 0.1%
1.2 4
 
< 0.1%
1.3 13
 
< 0.1%
1.4 5
 
< 0.1%
1.5 30
 
0.1%
1.6 6
 
< 0.1%
ValueCountFrequency (%)
10 190
0.4%
9.8 1
 
< 0.1%
9.6 1
 
< 0.1%
9.5 18
 
< 0.1%
9.4 3
 
< 0.1%
9.3 18
 
< 0.1%
9.2 4
 
< 0.1%
9.1 3
 
< 0.1%
9 159
0.4%
8.9 7
 
< 0.1%

vote_count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1820
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.96255
Minimum0
Maximum14075
Zeros2890
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size709.8 KiB
2023-06-24T16:31:17.551447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q334
95-th percentile434
Maximum14075
Range14075
Interquartile range (IQR)31

Descriptive statistics

Standard deviation491.48588
Coefficient of variation (CV)4.4695749
Kurtosis151.09239
Mean109.96255
Median Absolute Deviation (MAD)8
Skewness10.446463
Sum4995159
Variance241558.37
MonotonicityNot monotonic
2023-06-24T16:31:17.805439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3257
 
7.2%
2 3130
 
6.9%
0 2890
 
6.4%
3 2783
 
6.1%
4 2477
 
5.5%
5 2096
 
4.6%
6 1747
 
3.8%
7 1570
 
3.5%
8 1359
 
3.0%
9 1194
 
2.6%
Other values (1810) 22923
50.5%
ValueCountFrequency (%)
0 2890
6.4%
1 3257
7.2%
2 3130
6.9%
3 2783
6.1%
4 2477
5.5%
5 2096
4.6%
6 1747
3.8%
7 1570
3.5%
8 1359
3.0%
9 1194
 
2.6%
ValueCountFrequency (%)
14075 1
< 0.1%
12269 1
< 0.1%
12114 1
< 0.1%
12000 1
< 0.1%
11444 1
< 0.1%
11187 1
< 0.1%
10297 1
< 0.1%
10014 1
< 0.1%
9678 1
< 0.1%
9634 1
< 0.1%

id_collection
Real number (ℝ)

MISSING 

Distinct1695
Distinct (%)37.8%
Missing40938
Missing (%)90.1%
Infinite0
Infinite (%)0.0%
Mean184101.31
Minimum10
Maximum480160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size709.8 KiB
2023-06-24T16:31:18.074284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile2704
Q186026.25
median141531.5
Q3294172
95-th percentile439530.55
Maximum480160
Range480150
Interquartile range (IQR)208145.75

Descriptive statistics

Standard deviation141637.8
Coefficient of variation (CV)0.76934705
Kurtosis-0.92843366
Mean184101.31
Median Absolute Deviation (MAD)104032.5
Skewness0.53250174
Sum8.2624667 × 108
Variance2.0061266 × 1010
MonotonicityNot monotonic
2023-06-24T16:31:18.352121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
415931 29
 
0.1%
421566 27
 
0.1%
645 26
 
0.1%
96887 26
 
0.1%
37261 25
 
0.1%
34055 22
 
< 0.1%
413661 21
 
< 0.1%
374509 16
 
< 0.1%
425164 15
 
< 0.1%
148324 15
 
< 0.1%
Other values (1685) 4266
 
9.4%
(Missing) 40938
90.1%
ValueCountFrequency (%)
10 8
< 0.1%
84 4
< 0.1%
119 3
 
< 0.1%
131 3
 
< 0.1%
151 6
< 0.1%
230 3
 
< 0.1%
263 3
 
< 0.1%
264 3
 
< 0.1%
295 5
< 0.1%
304 3
 
< 0.1%
ValueCountFrequency (%)
480160 1
 
< 0.1%
480071 1
 
< 0.1%
479971 1
 
< 0.1%
479888 2
 
< 0.1%
479692 2
 
< 0.1%
479549 1
 
< 0.1%
479319 13
< 0.1%
478947 2
 
< 0.1%
478628 12
< 0.1%
478545 1
 
< 0.1%

name_collection
Text

MISSING 

Distinct1695
Distinct (%)37.8%
Missing40938
Missing (%)90.1%
Memory size709.8 KiB
2023-06-24T16:31:18.874898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length54
Median length43
Mean length23.85762
Min length3

Characters and Unicode

Total characters107073
Distinct characters166
Distinct categories12 ?
Distinct scripts7 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique390 ?
Unique (%)8.7%

Sample

1st rowToy Story Collection
2nd rowGrumpy Old Men Collection
3rd rowFather of the Bride Collection
4th rowJames Bond Collection
5th rowBalto Collection
ValueCountFrequency (%)
collection 3744
25.3%
the 1146
 
7.8%
of 230
 
1.6%
series 147
 
1.0%
139
 
0.9%
trilogy 87
 
0.6%
and 84
 
0.6%
a 62
 
0.4%
man 62
 
0.4%
in 56
 
0.4%
Other values (2407) 9026
61.1%
2023-06-24T16:31:19.717643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 11116
 
10.4%
e 10452
 
9.8%
10296
 
9.6%
l 10202
 
9.5%
i 7560
 
7.1%
n 7405
 
6.9%
t 6489
 
6.1%
c 4846
 
4.5%
C 4475
 
4.2%
a 4459
 
4.2%
Other values (156) 29773
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 81113
75.8%
Uppercase Letter 13884
 
13.0%
Space Separator 10296
 
9.6%
Other Punctuation 576
 
0.5%
Open Punctuation 335
 
0.3%
Close Punctuation 335
 
0.3%
Decimal Number 321
 
0.3%
Dash Punctuation 162
 
0.2%
Other Letter 37
 
< 0.1%
Final Punctuation 9
 
< 0.1%
Other values (2) 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 11116
13.7%
e 10452
12.9%
l 10202
12.6%
i 7560
9.3%
n 7405
9.1%
t 6489
8.0%
c 4846
 
6.0%
a 4459
 
5.5%
r 3871
 
4.8%
s 2588
 
3.2%
Other values (69) 12125
14.9%
Uppercase Letter
ValueCountFrequency (%)
C 4475
32.2%
T 1527
 
11.0%
S 1063
 
7.7%
B 682
 
4.9%
M 630
 
4.5%
A 509
 
3.7%
D 506
 
3.6%
H 462
 
3.3%
P 432
 
3.1%
G 417
 
3.0%
Other values (33) 3181
22.9%
Other Letter
ValueCountFrequency (%)
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
2
 
5.4%
Other values (4) 8
21.6%
Other Punctuation
ValueCountFrequency (%)
. 172
29.9%
' 107
18.6%
: 99
17.2%
, 79
13.7%
& 52
 
9.0%
! 35
 
6.1%
/ 21
 
3.6%
? 4
 
0.7%
* 4
 
0.7%
3
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 80
24.9%
9 64
19.9%
3 54
16.8%
0 51
15.9%
2 21
 
6.5%
8 13
 
4.0%
5 12
 
3.7%
7 11
 
3.4%
6 10
 
3.1%
4 5
 
1.6%
Open Punctuation
ValueCountFrequency (%)
( 330
98.5%
[ 5
 
1.5%
Close Punctuation
ValueCountFrequency (%)
) 330
98.5%
] 5
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 160
98.8%
2
 
1.2%
Space Separator
ValueCountFrequency (%)
10296
100.0%
Final Punctuation
ValueCountFrequency (%)
9
100.0%
Modifier Letter
ValueCountFrequency (%)
3
100.0%
Other Number
ValueCountFrequency (%)
½ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 94583
88.3%
Common 12039
 
11.2%
Cyrillic 414
 
0.4%
Hiragana 15
 
< 0.1%
Hangul 10
 
< 0.1%
Katakana 9
 
< 0.1%
Han 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 11116
11.8%
e 10452
11.1%
l 10202
10.8%
i 7560
 
8.0%
n 7405
 
7.8%
t 6489
 
6.9%
c 4846
 
5.1%
C 4475
 
4.7%
a 4459
 
4.7%
r 3871
 
4.1%
Other values (70) 23708
25.1%
Cyrillic
ValueCountFrequency (%)
л 48
 
11.6%
и 41
 
9.9%
о 37
 
8.9%
к 30
 
7.2%
е 27
 
6.5%
я 25
 
6.0%
а 17
 
4.1%
ц 16
 
3.9%
К 16
 
3.9%
р 14
 
3.4%
Other values (32) 143
34.5%
Common
ValueCountFrequency (%)
10296
85.5%
( 330
 
2.7%
) 330
 
2.7%
. 172
 
1.4%
- 160
 
1.3%
' 107
 
0.9%
: 99
 
0.8%
1 80
 
0.7%
, 79
 
0.7%
9 64
 
0.5%
Other values (20) 322
 
2.7%
Hiragana
ValueCountFrequency (%)
3
20.0%
3
20.0%
3
20.0%
3
20.0%
3
20.0%
Hangul
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
2
20.0%
Katakana
ValueCountFrequency (%)
3
33.3%
3
33.3%
3
33.3%
Han
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106359
99.3%
Cyrillic 414
 
0.4%
None 246
 
0.2%
Hiragana 15
 
< 0.1%
Punctuation 14
 
< 0.1%
Katakana 12
 
< 0.1%
Hangul 10
 
< 0.1%
CJK 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 11116
 
10.5%
e 10452
 
9.8%
10296
 
9.7%
l 10202
 
9.6%
i 7560
 
7.1%
n 7405
 
7.0%
t 6489
 
6.1%
c 4846
 
4.6%
C 4475
 
4.2%
a 4459
 
4.2%
Other values (67) 29059
27.3%
Cyrillic
ValueCountFrequency (%)
л 48
 
11.6%
и 41
 
9.9%
о 37
 
8.9%
к 30
 
7.2%
е 27
 
6.5%
я 25
 
6.0%
а 17
 
4.1%
ц 16
 
3.9%
К 16
 
3.9%
р 14
 
3.4%
Other values (32) 143
34.5%
None
ValueCountFrequency (%)
é 45
18.3%
ä 40
16.3%
ô 35
14.2%
ò 28
11.4%
ö 19
7.7%
ı 14
 
5.7%
ó 14
 
5.7%
í 9
 
3.7%
á 4
 
1.6%
İ 4
 
1.6%
Other values (19) 34
13.8%
Punctuation
ValueCountFrequency (%)
9
64.3%
3
 
21.4%
2
 
14.3%
Hiragana
ValueCountFrequency (%)
3
20.0%
3
20.0%
3
20.0%
3
20.0%
3
20.0%
Katakana
ValueCountFrequency (%)
3
25.0%
3
25.0%
3
25.0%
3
25.0%
CJK
ValueCountFrequency (%)
3
100.0%
Hangul
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
2
20.0%
Distinct1403
Distinct (%)35.6%
Missing41481
Missing (%)91.3%
Memory size709.8 KiB
2023-06-24T16:31:20.107425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length32
Mean length31.958935
Min length31

Characters and Unicode

Total characters126078
Distinct characters64
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique254 ?
Unique (%)6.4%

Sample

1st row/7G9915LfUQ2lVfwMEEhDsn3kT4B.jpg
2nd row/nLvUdqgPgm3F85NMCii9gVFUcet.jpg
3rd row/nts4iOmNnq7GNicycMJ9pSAn204.jpg
4th row/HORpg5CSkmeQlAolx3bKMrKgfi.jpg
5th row/w0ZgH6Lgxt2bQYnf1ss74UvYftm.jpg
ValueCountFrequency (%)
q6sa4bzmt9ck7eemxywt7pnrl5h.jpg 29
 
0.7%
4ayjsjc3djgwu9ecwuokdbwvdlc.jpg 27
 
0.7%
horpg5cskmeqlaolx3bkmrkgfi.jpg 26
 
0.7%
8q31datmfjjhftwqgxghbucgwk2.jpg 26
 
0.7%
2p0hnrygkdvirv8rcdt1rbsjdbj.jpg 25
 
0.6%
j5te0ynzamxdbnsqtudkibet8iu.jpg 22
 
0.6%
y0xwqplrattvypzxf5ziuipsd2u.jpg 21
 
0.5%
scvws6k8giw8w24ucmepqqvl10l.jpg 16
 
0.4%
2vmz1zrfpnuqtqp5k4wrxvdybjh.jpg 15
 
0.4%
esddu6pbocmayu1sxqfu9vyyoq6.jpg 15
 
0.4%
Other values (1393) 3723
94.4%
2023-06-24T16:31:20.736083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
g 5834
 
4.6%
p 5750
 
4.6%
j 5612
 
4.5%
/ 3945
 
3.1%
. 3945
 
3.1%
5 1928
 
1.5%
d 1907
 
1.5%
m 1875
 
1.5%
l 1846
 
1.5%
k 1844
 
1.5%
Other values (54) 91592
72.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 57404
45.5%
Uppercase Letter 43540
34.5%
Decimal Number 17244
 
13.7%
Other Punctuation 7890
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g 5834
 
10.2%
p 5750
 
10.0%
j 5612
 
9.8%
d 1907
 
3.3%
m 1875
 
3.3%
l 1846
 
3.2%
k 1844
 
3.2%
u 1836
 
3.2%
c 1802
 
3.1%
t 1792
 
3.1%
Other values (16) 27306
47.6%
Uppercase Letter
ValueCountFrequency (%)
C 1832
 
4.2%
D 1831
 
4.2%
F 1805
 
4.1%
U 1794
 
4.1%
Q 1791
 
4.1%
K 1752
 
4.0%
B 1748
 
4.0%
S 1735
 
4.0%
J 1696
 
3.9%
L 1687
 
3.9%
Other values (16) 25869
59.4%
Decimal Number
ValueCountFrequency (%)
5 1928
11.2%
2 1835
10.6%
7 1751
10.2%
4 1727
10.0%
9 1713
9.9%
1 1703
9.9%
6 1674
9.7%
8 1663
9.6%
3 1662
9.6%
0 1588
9.2%
Other Punctuation
ValueCountFrequency (%)
/ 3945
50.0%
. 3945
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 100944
80.1%
Common 25134
 
19.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
g 5834
 
5.8%
p 5750
 
5.7%
j 5612
 
5.6%
d 1907
 
1.9%
m 1875
 
1.9%
l 1846
 
1.8%
k 1844
 
1.8%
u 1836
 
1.8%
C 1832
 
1.8%
D 1831
 
1.8%
Other values (42) 70777
70.1%
Common
ValueCountFrequency (%)
/ 3945
15.7%
. 3945
15.7%
5 1928
7.7%
2 1835
7.3%
7 1751
7.0%
4 1727
6.9%
9 1713
6.8%
1 1703
6.8%
6 1674
6.7%
8 1663
6.6%
Other values (2) 3250
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 126078
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
g 5834
 
4.6%
p 5750
 
4.6%
j 5612
 
4.5%
/ 3945
 
3.1%
. 3945
 
3.1%
5 1928
 
1.5%
d 1907
 
1.5%
m 1875
 
1.5%
l 1846
 
1.5%
k 1844
 
1.5%
Other values (54) 91592
72.6%
Distinct1122
Distinct (%)34.4%
Missing42165
Missing (%)92.8%
Memory size709.8 KiB
2023-06-24T16:31:21.114877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length32
Mean length31.974241
Min length31

Characters and Unicode

Total characters104268
Distinct characters64
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique182 ?
Unique (%)5.6%

Sample

1st row/9FBwqcd9IRruEDUrTdcaafOMKUq.jpg
2nd row/hypTnLot2z8wpFS7qwsQHW1uV8u.jpg
3rd row/7qwE57OVZmMJChBpLEbJEmzUydk.jpg
4th row/6VcVl48kNKvdXOZfJPdarlUGOsk.jpg
5th row/9VM5LiJV0bGb1st1KyHA3cVnO2G.jpg
ValueCountFrequency (%)
foe3kuijmg5aklhtd3skwbatmf2.jpg 29
 
0.9%
jauuprubvaxxlay5hufrnjxccuh.jpg 27
 
0.8%
6vcvl48knkvdxozfjpdarlugosk.jpg 26
 
0.8%
by8glimmr5pr9pag3zpobfaaq8n.jpg 26
 
0.8%
38tf1ljn7ulezauafp7beapmfcl.jpg 25
 
0.8%
igoyka0tffgsozpg2u5vitjmgfk.jpg 22
 
0.7%
dx9ysup5zeojxywg4ukybvazixo.jpg 16
 
0.5%
9be62qbanbftoiic9cxjk1xw3w.jpg 15
 
0.5%
7pcbijxtfwi9vjwefxds0reaw8q.jpg 15
 
0.5%
kdspcm1qx5zjpnaz80dbshdtowh.jpg 14
 
0.4%
Other values (1112) 3046
93.4%
2023-06-24T16:31:21.722492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
j 4712
 
4.5%
p 4708
 
4.5%
g 4674
 
4.5%
/ 3261
 
3.1%
. 3261
 
3.1%
k 1735
 
1.7%
c 1686
 
1.6%
f 1673
 
1.6%
u 1595
 
1.5%
8 1570
 
1.5%
Other values (54) 75393
72.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 47409
45.5%
Uppercase Letter 35782
34.3%
Decimal Number 14555
 
14.0%
Other Punctuation 6522
 
6.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
j 4712
 
9.9%
p 4708
 
9.9%
g 4674
 
9.9%
k 1735
 
3.7%
c 1686
 
3.6%
f 1673
 
3.5%
u 1595
 
3.4%
a 1558
 
3.3%
b 1519
 
3.2%
i 1516
 
3.2%
Other values (16) 22033
46.5%
Uppercase Letter
ValueCountFrequency (%)
A 1546
 
4.3%
Z 1518
 
4.2%
T 1488
 
4.2%
U 1470
 
4.1%
K 1449
 
4.0%
N 1441
 
4.0%
M 1440
 
4.0%
G 1434
 
4.0%
Y 1431
 
4.0%
L 1413
 
3.9%
Other values (16) 21152
59.1%
Decimal Number
ValueCountFrequency (%)
8 1570
10.8%
9 1541
10.6%
5 1500
10.3%
1 1470
10.1%
7 1466
10.1%
2 1456
10.0%
3 1453
10.0%
0 1435
9.9%
6 1390
9.5%
4 1274
8.8%
Other Punctuation
ValueCountFrequency (%)
/ 3261
50.0%
. 3261
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 83191
79.8%
Common 21077
 
20.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
j 4712
 
5.7%
p 4708
 
5.7%
g 4674
 
5.6%
k 1735
 
2.1%
c 1686
 
2.0%
f 1673
 
2.0%
u 1595
 
1.9%
a 1558
 
1.9%
A 1546
 
1.9%
b 1519
 
1.8%
Other values (42) 57785
69.5%
Common
ValueCountFrequency (%)
/ 3261
15.5%
. 3261
15.5%
8 1570
7.4%
9 1541
7.3%
5 1500
7.1%
1 1470
7.0%
7 1466
7.0%
2 1456
6.9%
3 1453
6.9%
0 1435
6.8%
Other values (2) 2664
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 104268
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
j 4712
 
4.5%
p 4708
 
4.5%
g 4674
 
4.5%
/ 3261
 
3.1%
. 3261
 
3.1%
k 1735
 
1.7%
c 1686
 
1.6%
f 1673
 
1.6%
u 1595
 
1.5%
8 1570
 
1.5%
Other values (54) 75393
72.3%

name_company
Text

MISSING 

Distinct22670
Distinct (%)67.5%
Missing11853
Missing (%)26.1%
Memory size709.8 KiB
2023-06-24T16:31:22.265471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length609
Median length412
Mean length41.484288
Min length2

Characters and Unicode

Total characters1392752
Distinct characters294
Distinct categories17 ?
Distinct scripts6 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20313 ?
Unique (%)60.5%

Sample

1st rowPixar Animation Studios
2nd rowTriStar Pictures, Teitler Film, Interscope Communications
3rd rowWarner Bros., Lancaster Gate
4th rowTwentieth Century Fox Film Corporation
5th rowSandollar Productions, Touchstone Pictures
ValueCountFrequency (%)
films 9451
 
5.3%
pictures 9266
 
5.2%
productions 9059
 
5.1%
film 6672
 
3.8%
entertainment 5155
 
2.9%
corporation 2190
 
1.2%
company 1768
 
1.0%
warner 1478
 
0.8%
bros 1411
 
0.8%
the 1379
 
0.8%
Other values (18619) 129774
73.1%
2023-06-24T16:31:23.118984image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
144039
 
10.3%
i 106892
 
7.7%
e 94596
 
6.8%
n 89937
 
6.5%
o 85266
 
6.1%
r 83514
 
6.0%
t 83404
 
6.0%
a 77105
 
5.5%
s 62647
 
4.5%
l 51224
 
3.7%
Other values (284) 514128
36.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 986580
70.8%
Uppercase Letter 198882
 
14.3%
Space Separator 144044
 
10.3%
Other Punctuation 45076
 
3.2%
Decimal Number 4348
 
0.3%
Dash Punctuation 4327
 
0.3%
Open Punctuation 4325
 
0.3%
Close Punctuation 4324
 
0.3%
Math Symbol 661
 
< 0.1%
Other Letter 140
 
< 0.1%
Other values (7) 45
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 106892
10.8%
e 94596
9.6%
n 89937
9.1%
o 85266
8.6%
r 83514
8.5%
t 83404
8.5%
a 77105
 
7.8%
s 62647
 
6.3%
l 51224
 
5.2%
m 44254
 
4.5%
Other values (102) 207741
21.1%
Other Letter
ValueCountFrequency (%)
9
 
6.4%
8
 
5.7%
6
 
4.3%
5
 
3.6%
5
 
3.6%
5
 
3.6%
5
 
3.6%
5
 
3.6%
4
 
2.9%
3
 
2.1%
Other values (62) 85
60.7%
Uppercase Letter
ValueCountFrequency (%)
P 27877
14.0%
F 26337
13.2%
C 20567
 
10.3%
M 13355
 
6.7%
S 11908
 
6.0%
E 9748
 
4.9%
A 9540
 
4.8%
T 9351
 
4.7%
B 9003
 
4.5%
G 7805
 
3.9%
Other values (52) 53391
26.8%
Other Punctuation
ValueCountFrequency (%)
, 37332
82.8%
. 5671
 
12.6%
& 765
 
1.7%
/ 643
 
1.4%
' 451
 
1.0%
" 133
 
0.3%
! 36
 
0.1%
% 18
 
< 0.1%
: 9
 
< 0.1%
@ 5
 
< 0.1%
Other values (6) 13
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 1035
23.8%
1 712
16.4%
0 641
14.7%
3 556
12.8%
4 481
11.1%
9 205
 
4.7%
6 195
 
4.5%
5 178
 
4.1%
8 173
 
4.0%
7 172
 
4.0%
Open Punctuation
ValueCountFrequency (%)
( 4315
99.8%
[ 9
 
0.2%
1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 4314
99.8%
] 9
 
0.2%
1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
144039
> 99.9%
  5
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 4325
> 99.9%
2
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 660
99.8%
| 1
 
0.2%
Other Symbol
ValueCountFrequency (%)
° 23
92.0%
2
 
8.0%
Final Punctuation
ValueCountFrequency (%)
3
50.0%
» 3
50.0%
Other Number
ValueCountFrequency (%)
½ 1
50.0%
² 1
50.0%
Control
ValueCountFrequency (%)
4
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Initial Punctuation
ValueCountFrequency (%)
« 3
100.0%
Format
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1185059
85.1%
Common 207148
 
14.9%
Cyrillic 373
 
< 0.1%
Hangul 115
 
< 0.1%
Greek 31
 
< 0.1%
Han 26
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 106892
 
9.0%
e 94596
 
8.0%
n 89937
 
7.6%
o 85266
 
7.2%
r 83514
 
7.0%
t 83404
 
7.0%
a 77105
 
6.5%
s 62647
 
5.3%
l 51224
 
4.3%
m 44254
 
3.7%
Other values (99) 406220
34.3%
Hangul
ValueCountFrequency (%)
9
 
7.8%
8
 
7.0%
6
 
5.2%
5
 
4.3%
5
 
4.3%
5
 
4.3%
5
 
4.3%
5
 
4.3%
4
 
3.5%
3
 
2.6%
Other values (43) 60
52.2%
Common
ValueCountFrequency (%)
144039
69.5%
, 37332
 
18.0%
. 5671
 
2.7%
- 4325
 
2.1%
( 4315
 
2.1%
) 4314
 
2.1%
2 1035
 
0.5%
& 765
 
0.4%
1 712
 
0.3%
+ 660
 
0.3%
Other values (37) 3980
 
1.9%
Cyrillic
ValueCountFrequency (%)
и 34
 
9.1%
о 28
 
7.5%
а 26
 
7.0%
л 22
 
5.9%
н 20
 
5.4%
м 19
 
5.1%
т 17
 
4.6%
е 16
 
4.3%
ь 16
 
4.3%
с 16
 
4.3%
Other values (36) 159
42.6%
Greek
ValueCountFrequency (%)
ν 3
 
9.7%
ο 3
 
9.7%
ι 2
 
6.5%
τ 2
 
6.5%
η 2
 
6.5%
ρ 2
 
6.5%
λ 2
 
6.5%
Κ 2
 
6.5%
Ε 2
 
6.5%
ά 1
 
3.2%
Other values (10) 10
32.3%
Han
ValueCountFrequency (%)
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
1
 
3.8%
1
 
3.8%
1
 
3.8%
Other values (9) 9
34.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1386528
99.6%
None 5705
 
0.4%
Cyrillic 373
 
< 0.1%
Hangul 113
 
< 0.1%
CJK 26
 
< 0.1%
Punctuation 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
144039
 
10.4%
i 106892
 
7.7%
e 94596
 
6.8%
n 89937
 
6.5%
o 85266
 
6.1%
r 83514
 
6.0%
t 83404
 
6.0%
a 77105
 
5.6%
s 62647
 
4.5%
l 51224
 
3.7%
Other values (77) 507904
36.6%
None
ValueCountFrequency (%)
é 3173
55.6%
ó 416
 
7.3%
á 317
 
5.6%
í 173
 
3.0%
ü 154
 
2.7%
ñ 150
 
2.6%
ô 140
 
2.5%
ä 136
 
2.4%
è 136
 
2.4%
ö 131
 
2.3%
Other values (76) 779
 
13.7%
Cyrillic
ValueCountFrequency (%)
и 34
 
9.1%
о 28
 
7.5%
а 26
 
7.0%
л 22
 
5.9%
н 20
 
5.4%
м 19
 
5.1%
т 17
 
4.6%
е 16
 
4.3%
ь 16
 
4.3%
с 16
 
4.3%
Other values (36) 159
42.6%
Hangul
ValueCountFrequency (%)
9
 
8.0%
8
 
7.1%
6
 
5.3%
5
 
4.4%
5
 
4.4%
5
 
4.4%
5
 
4.4%
5
 
4.4%
4
 
3.5%
3
 
2.7%
Other values (42) 58
51.3%
Punctuation
ValueCountFrequency (%)
3
42.9%
2
28.6%
1
 
14.3%
1
 
14.3%
CJK
ValueCountFrequency (%)
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
1
 
3.8%
1
 
3.8%
1
 
3.8%
Other values (9) 9
34.6%

id_company
Text

MISSING 

Distinct22706
Distinct (%)67.6%
Missing11853
Missing (%)26.1%
Memory size709.8 KiB
2023-06-24T16:31:23.698670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length171
Median length153
Mean length10.873619
Min length1

Characters and Unicode

Total characters365060
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20354 ?
Unique (%)60.6%

Sample

1st row3
2nd row559, 2550, 10201
3rd row6194, 19464
4th row306
5th row5842, 9195
ValueCountFrequency (%)
6194 1250
 
1.8%
8411 1075
 
1.5%
4 1003
 
1.4%
306 836
 
1.2%
33 830
 
1.2%
441 448
 
0.6%
5358 436
 
0.6%
5 431
 
0.6%
6 290
 
0.4%
60 279
 
0.4%
Other values (23682) 63624
90.2%
2023-06-24T16:31:24.586139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 44333
12.1%
, 36929
10.1%
36929
10.1%
2 32502
8.9%
3 31274
8.6%
4 30188
8.3%
6 27878
7.6%
5 27613
7.6%
8 25668
7.0%
7 24359
6.7%
Other values (2) 47387
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 291202
79.8%
Other Punctuation 36929
 
10.1%
Space Separator 36929
 
10.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 44333
15.2%
2 32502
11.2%
3 31274
10.7%
4 30188
10.4%
6 27878
9.6%
5 27613
9.5%
8 25668
8.8%
7 24359
8.4%
9 24148
8.3%
0 23239
8.0%
Other Punctuation
ValueCountFrequency (%)
, 36929
100.0%
Space Separator
ValueCountFrequency (%)
36929
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 365060
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 44333
12.1%
, 36929
10.1%
36929
10.1%
2 32502
8.9%
3 31274
8.6%
4 30188
8.3%
6 27878
7.6%
5 27613
7.6%
8 25668
7.0%
7 24359
6.7%
Other values (2) 47387
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 365060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 44333
12.1%
, 36929
10.1%
36929
10.1%
2 32502
8.9%
3 31274
8.6%
4 30188
8.3%
6 27878
7.6%
5 27613
7.6%
8 25668
7.0%
7 24359
6.7%
Other values (2) 47387
13.0%
Distinct4065
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size709.8 KiB
2023-06-24T16:31:24.865690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length98
Median length80
Mean length21.5916
Min length2

Characters and Unicode

Total characters980820
Distinct characters33
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2364 ?
Unique (%)5.2%

Sample

1st row['Animation', 'Comedy', 'Family']
2nd row['Adventure', 'Fantasy', 'Family']
3rd row['Romance', 'Comedy']
4th row['Comedy', 'Drama', 'Romance']
5th row['Comedy']
ValueCountFrequency (%)
drama 20253
20.8%
comedy 13178
13.5%
thriller 7620
 
7.8%
romance 6734
 
6.9%
action 6592
 
6.8%
horror 4670
 
4.8%
crime 4306
 
4.4%
documentary 3929
 
4.0%
adventure 3495
 
3.6%
science 3043
 
3.1%
Other values (13) 23460
24.1%
2023-06-24T16:31:25.424697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 182088
18.6%
r 69081
 
7.0%
a 61815
 
6.3%
e 55778
 
5.7%
m 53101
 
5.4%
51854
 
5.3%
o 48531
 
4.9%
, 48045
 
4.9%
[ 45426
 
4.6%
] 45426
 
4.6%
Other values (23) 319675
32.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 512362
52.2%
Other Punctuation 230133
23.5%
Uppercase Letter 95619
 
9.7%
Space Separator 51854
 
5.3%
Open Punctuation 45426
 
4.6%
Close Punctuation 45426
 
4.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 69081
13.5%
a 61815
12.1%
e 55778
10.9%
m 53101
10.4%
o 48531
9.5%
i 39661
7.7%
n 35671
7.0%
y 28516
5.6%
c 27982
5.5%
t 26206
 
5.1%
Other values (7) 66020
12.9%
Uppercase Letter
ValueCountFrequency (%)
D 24182
25.3%
C 17484
18.3%
A 12018
12.6%
F 9744
10.2%
T 8386
 
8.8%
R 6734
 
7.0%
H 6068
 
6.3%
M 4829
 
5.1%
S 3043
 
3.2%
W 2365
 
2.5%
Other Punctuation
ValueCountFrequency (%)
' 182088
79.1%
, 48045
 
20.9%
Space Separator
ValueCountFrequency (%)
51854
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 45426
100.0%
Close Punctuation
ValueCountFrequency (%)
] 45426
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 607981
62.0%
Common 372839
38.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 69081
11.4%
a 61815
 
10.2%
e 55778
 
9.2%
m 53101
 
8.7%
o 48531
 
8.0%
i 39661
 
6.5%
n 35671
 
5.9%
y 28516
 
4.7%
c 27982
 
4.6%
t 26206
 
4.3%
Other values (18) 161639
26.6%
Common
ValueCountFrequency (%)
' 182088
48.8%
51854
 
13.9%
, 48045
 
12.9%
[ 45426
 
12.2%
] 45426
 
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 980820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 182088
18.6%
r 69081
 
7.0%
a 61815
 
6.3%
e 55778
 
5.7%
m 53101
 
5.4%
51854
 
5.3%
o 48531
 
4.9%
, 48045
 
4.9%
[ 45426
 
4.6%
] 45426
 
4.6%
Other values (23) 319675
32.6%
Distinct4065
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size709.8 KiB
2023-06-24T16:31:25.675533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length42
Median length37
Mean length9.2774182
Min length2

Characters and Unicode

Total characters421436
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2364 ?
Unique (%)5.2%

Sample

1st row[16, 35, 10751]
2nd row[12, 14, 10751]
3rd row[10749, 35]
4th row[35, 18, 10749]
5th row[35]
ValueCountFrequency (%)
18 20253
21.7%
35 13178
14.1%
53 7620
 
8.2%
10749 6734
 
7.2%
28 6592
 
7.1%
27 4670
 
5.0%
80 4306
 
4.6%
99 3929
 
4.2%
12 3495
 
3.7%
878 3043
 
3.3%
Other values (11) 19651
21.0%
2023-06-24T16:31:26.263447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 48045
11.4%
48045
11.4%
1 45571
10.8%
[ 45426
10.8%
] 45426
10.8%
8 39702
9.4%
5 24891
5.9%
3 23238
5.5%
7 22734
 
5.4%
0 21481
 
5.1%
Other values (4) 56877
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 234494
55.6%
Other Punctuation 48045
 
11.4%
Space Separator 48045
 
11.4%
Open Punctuation 45426
 
10.8%
Close Punctuation 45426
 
10.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 45571
19.4%
8 39702
16.9%
5 24891
10.6%
3 23238
9.9%
7 22734
9.7%
0 21481
9.2%
9 18677
8.0%
2 17678
 
7.5%
4 13108
 
5.6%
6 7414
 
3.2%
Other Punctuation
ValueCountFrequency (%)
, 48045
100.0%
Space Separator
ValueCountFrequency (%)
48045
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 45426
100.0%
Close Punctuation
ValueCountFrequency (%)
] 45426
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 421436
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 48045
11.4%
48045
11.4%
1 45571
10.8%
[ 45426
10.8%
] 45426
10.8%
8 39702
9.4%
5 24891
5.9%
3 23238
5.5%
7 22734
 
5.4%
0 21481
 
5.1%
Other values (4) 56877
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 421436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 48045
11.4%
48045
11.4%
1 45571
10.8%
[ 45426
10.8%
] 45426
10.8%
8 39702
9.4%
5 24891
5.9%
3 23238
5.5%
7 22734
 
5.4%
0 21481
 
5.1%
Other values (4) 56877
13.5%
Distinct2390
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size709.8 KiB
2023-06-24T16:31:26.639229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length150
Median length6
Mean length6.7988377
Min length2

Characters and Unicode

Total characters308844
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1769 ?
Unique (%)3.9%

Sample

1st row['US']
2nd row['US']
3rd row['US']
4th row['US']
5th row['US']
ValueCountFrequency (%)
us 21148
38.0%
6264
 
11.3%
gb 4089
 
7.3%
fr 3935
 
7.1%
de 2250
 
4.0%
it 2168
 
3.9%
ca 1765
 
3.2%
jp 1648
 
3.0%
es 964
 
1.7%
ru 912
 
1.6%
Other values (152) 10507
18.9%
2023-06-24T16:31:27.698015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 98772
32.0%
[ 45426
14.7%
] 45426
14.7%
S 23040
 
7.5%
U 23025
 
7.5%
10224
 
3.3%
, 10224
 
3.3%
R 6683
 
2.2%
B 4979
 
1.6%
E 4745
 
1.5%
Other values (21) 36300
 
11.8%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 108996
35.3%
Uppercase Letter 98772
32.0%
Open Punctuation 45426
14.7%
Close Punctuation 45426
14.7%
Space Separator 10224
 
3.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 23040
23.3%
U 23025
23.3%
R 6683
 
6.8%
B 4979
 
5.0%
E 4745
 
4.8%
G 4446
 
4.5%
F 4336
 
4.4%
I 4008
 
4.1%
A 3136
 
3.2%
T 3006
 
3.0%
Other values (16) 17368
17.6%
Other Punctuation
ValueCountFrequency (%)
' 98772
90.6%
, 10224
 
9.4%
Open Punctuation
ValueCountFrequency (%)
[ 45426
100.0%
Close Punctuation
ValueCountFrequency (%)
] 45426
100.0%
Space Separator
ValueCountFrequency (%)
10224
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210072
68.0%
Latin 98772
32.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 23040
23.3%
U 23025
23.3%
R 6683
 
6.8%
B 4979
 
5.0%
E 4745
 
4.8%
G 4446
 
4.5%
F 4336
 
4.4%
I 4008
 
4.1%
A 3136
 
3.2%
T 3006
 
3.0%
Other values (16) 17368
17.6%
Common
ValueCountFrequency (%)
' 98772
47.0%
[ 45426
21.6%
] 45426
21.6%
10224
 
4.9%
, 10224
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308844
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 98772
32.0%
[ 45426
14.7%
] 45426
14.7%
S 23040
 
7.5%
U 23025
 
7.5%
10224
 
3.3%
, 10224
 
3.3%
R 6683
 
2.2%
B 4979
 
1.6%
E 4745
 
1.5%
Other values (21) 36300
 
11.8%
Distinct2390
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size709.8 KiB
2023-06-24T16:31:28.137765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length289
Median length199
Mean length20.59171
Min length2

Characters and Unicode

Total characters935399
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1769 ?
Unique (%)3.9%

Sample

1st row['United States of America']
2nd row['United States of America']
3rd row['United States of America']
4th row['United States of America']
5th row['United States of America']
ValueCountFrequency (%)
united 25265
20.2%
states 21149
16.9%
of 21148
16.9%
america 21148
16.9%
6264
 
5.0%
kingdom 4089
 
3.3%
france 3935
 
3.1%
germany 2256
 
1.8%
italy 2168
 
1.7%
canada 1765
 
1.4%
Other values (178) 15809
12.6%
2023-06-24T16:31:28.918336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 98767
 
10.6%
e 80632
 
8.6%
79570
 
8.5%
t 72619
 
7.8%
a 70471
 
7.5%
i 58541
 
6.3%
n 47475
 
5.1%
] 45426
 
4.9%
[ 45426
 
4.9%
d 34536
 
3.7%
Other values (46) 301936
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 558430
59.7%
Other Punctuation 109001
 
11.7%
Uppercase Letter 97546
 
10.4%
Space Separator 79570
 
8.5%
Close Punctuation 45426
 
4.9%
Open Punctuation 45426
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 80632
14.4%
t 72619
13.0%
a 70471
12.6%
i 58541
10.5%
n 47475
8.5%
d 34536
6.2%
r 32477
5.8%
o 29572
 
5.3%
m 28695
 
5.1%
c 26367
 
4.7%
Other values (16) 77045
13.8%
Uppercase Letter
ValueCountFrequency (%)
U 25366
26.0%
S 23834
24.4%
A 22390
23.0%
K 5215
 
5.3%
F 4328
 
4.4%
I 3585
 
3.7%
C 2594
 
2.7%
G 2469
 
2.5%
J 1664
 
1.7%
R 1308
 
1.3%
Other values (14) 4793
 
4.9%
Other Punctuation
ValueCountFrequency (%)
' 98767
90.6%
, 10224
 
9.4%
" 10
 
< 0.1%
Space Separator
ValueCountFrequency (%)
79570
100.0%
Close Punctuation
ValueCountFrequency (%)
] 45426
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 45426
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 655976
70.1%
Common 279423
29.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 80632
12.3%
t 72619
11.1%
a 70471
10.7%
i 58541
 
8.9%
n 47475
 
7.2%
d 34536
 
5.3%
r 32477
 
5.0%
o 29572
 
4.5%
m 28695
 
4.4%
c 26367
 
4.0%
Other values (40) 174591
26.6%
Common
ValueCountFrequency (%)
' 98767
35.3%
79570
28.5%
] 45426
16.3%
[ 45426
16.3%
, 10224
 
3.7%
" 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 935399
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 98767
 
10.6%
e 80632
 
8.6%
79570
 
8.5%
t 72619
 
7.8%
a 70471
 
7.5%
i 58541
 
6.3%
n 47475
 
5.1%
] 45426
 
4.9%
[ 45426
 
4.9%
d 34536
 
3.7%
Other values (46) 301936
32.3%
Distinct1931
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size709.8 KiB
2023-06-24T16:31:29.266112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length114
Median length6
Mean length7.2049047
Min length2

Characters and Unicode

Total characters327290
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1367 ?
Unique (%)3.0%

Sample

1st row['en']
2nd row['en', 'fr']
3rd row['en']
4th row['en']
5th row['en']
ValueCountFrequency (%)
en 28733
50.3%
fr 4194
 
7.3%
3814
 
6.7%
de 2623
 
4.6%
es 2413
 
4.2%
it 2367
 
4.1%
ja 1758
 
3.1%
ru 1563
 
2.7%
zh 790
 
1.4%
hi 706
 
1.2%
Other values (124) 8130
 
14.2%
2023-06-24T16:31:29.850932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 106554
32.6%
[ 45426
13.9%
] 45426
13.9%
e 34363
 
10.5%
n 29810
 
9.1%
, 11665
 
3.6%
11665
 
3.6%
r 6737
 
2.1%
f 4739
 
1.4%
t 3713
 
1.1%
Other values (21) 27192
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 118219
36.1%
Lowercase Letter 106554
32.6%
Open Punctuation 45426
 
13.9%
Close Punctuation 45426
 
13.9%
Space Separator 11665
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 34363
32.2%
n 29810
28.0%
r 6737
 
6.3%
f 4739
 
4.4%
t 3713
 
3.5%
i 3688
 
3.5%
s 3630
 
3.4%
d 2986
 
2.8%
a 2951
 
2.8%
h 2353
 
2.2%
Other values (16) 11584
 
10.9%
Other Punctuation
ValueCountFrequency (%)
' 106554
90.1%
, 11665
 
9.9%
Open Punctuation
ValueCountFrequency (%)
[ 45426
100.0%
Close Punctuation
ValueCountFrequency (%)
] 45426
100.0%
Space Separator
ValueCountFrequency (%)
11665
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 220736
67.4%
Latin 106554
32.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 34363
32.2%
n 29810
28.0%
r 6737
 
6.3%
f 4739
 
4.4%
t 3713
 
3.5%
i 3688
 
3.5%
s 3630
 
3.4%
d 2986
 
2.8%
a 2951
 
2.8%
h 2353
 
2.2%
Other values (16) 11584
 
10.9%
Common
ValueCountFrequency (%)
' 106554
48.3%
[ 45426
20.6%
] 45426
20.6%
, 11665
 
5.3%
11665
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 327290
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 106554
32.6%
[ 45426
13.9%
] 45426
13.9%
e 34363
 
10.5%
n 29810
 
9.1%
, 11665
 
3.6%
11665
 
3.6%
r 6737
 
2.1%
f 4739
 
1.4%
t 3713
 
1.1%
Other values (21) 27192
 
8.3%
Distinct1843
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size709.8 KiB
2023-06-24T16:31:30.196716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length214
Median length11
Mean length12.930568
Min length2

Characters and Unicode

Total characters587384
Distinct characters176
Distinct categories10 ?
Distinct scripts15 ?
Distinct blocks16 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1294 ?
Unique (%)2.8%

Sample

1st row['English']
2nd row['English', 'Français']
3rd row['English']
4th row['English']
5th row['English']
ValueCountFrequency (%)
english 28733
49.1%
4794
 
8.2%
français 4194
 
7.2%
deutsch 2623
 
4.5%
español 2413
 
4.1%
italiano 2367
 
4.0%
日本語 1758
 
3.0%
pусский 1563
 
2.7%
普通话 790
 
1.4%
हिन्दी 706
 
1.2%
Other values (69) 8563
 
14.6%
2023-06-24T16:31:30.837346image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 106554
18.1%
[ 45426
 
7.7%
] 45426
 
7.7%
s 42269
 
7.2%
n 37466
 
6.4%
i 37113
 
6.3%
l 34638
 
5.9%
h 31462
 
5.4%
E 31203
 
5.3%
g 30418
 
5.2%
Other values (166) 145409
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 292113
49.7%
Other Punctuation 119310
20.3%
Uppercase Letter 46435
 
7.9%
Open Punctuation 45426
 
7.7%
Close Punctuation 45426
 
7.7%
Other Letter 22186
 
3.8%
Space Separator 13078
 
2.2%
Spacing Mark 1836
 
0.3%
Nonspacing Mark 1548
 
0.3%
Decimal Number 26
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 42269
14.5%
n 37466
12.8%
i 37113
12.7%
l 34638
11.9%
h 31462
10.8%
g 30418
10.4%
a 18977
6.5%
o 7052
 
2.4%
r 6129
 
2.1%
t 5978
 
2.0%
Other values (64) 40611
13.9%
Other Letter
ValueCountFrequency (%)
1758
 
7.9%
1758
 
7.9%
1758
 
7.9%
1263
 
5.7%
946
 
4.3%
790
 
3.6%
790
 
3.6%
706
 
3.2%
706
 
3.2%
706
 
3.2%
Other values (46) 11005
49.6%
Uppercase Letter
ValueCountFrequency (%)
E 31203
67.2%
F 4196
 
9.0%
D 2924
 
6.3%
P 2678
 
5.8%
I 2367
 
5.1%
N 829
 
1.8%
L 506
 
1.1%
M 363
 
0.8%
T 308
 
0.7%
Č 284
 
0.6%
Other values (13) 777
 
1.7%
Spacing Mark
ValueCountFrequency (%)
706
38.5%
ि 706
38.5%
136
 
7.4%
ி 111
 
6.0%
94
 
5.1%
47
 
2.6%
18
 
1.0%
18
 
1.0%
Nonspacing Mark
ValueCountFrequency (%)
706
45.6%
ִ 430
27.8%
ְ 215
 
13.9%
111
 
7.2%
68
 
4.4%
18
 
1.2%
Other Punctuation
ValueCountFrequency (%)
' 106554
89.3%
, 11665
 
9.8%
/ 1015
 
0.9%
? 50
 
< 0.1%
\ 26
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
[ 45426
100.0%
Close Punctuation
ValueCountFrequency (%)
] 45426
100.0%
Space Separator
ValueCountFrequency (%)
13078
100.0%
Decimal Number
ValueCountFrequency (%)
9 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 326153
55.5%
Common 223266
38.0%
Han 10482
 
1.8%
Cyrillic 10460
 
1.8%
Devanagari 4236
 
0.7%
Arabic 3349
 
0.6%
Hangul 3252
 
0.6%
Hebrew 1720
 
0.3%
Greek 1704
 
0.3%
Thai 1225
 
0.2%
Other values (5) 1537
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 42269
13.0%
n 37466
11.5%
i 37113
11.4%
l 34638
10.6%
h 31462
9.6%
E 31203
9.6%
g 30418
9.3%
a 18977
 
5.8%
o 7052
 
2.2%
r 6129
 
1.9%
Other values (51) 49426
15.2%
Cyrillic
ValueCountFrequency (%)
с 3213
30.7%
к 1735
16.6%
и 1680
16.1%
й 1616
15.4%
у 1565
15.0%
а 113
 
1.1%
р 87
 
0.8%
ь 53
 
0.5%
н 53
 
0.5%
ї 53
 
0.5%
Other values (12) 292
 
2.8%
Arabic
ValueCountFrequency (%)
ا 538
16.1%
ر 538
16.1%
ب 341
10.2%
ة 341
10.2%
ي 341
10.2%
ع 341
10.2%
ل 341
10.2%
ی 142
 
4.2%
ف 142
 
4.2%
س 142
 
4.2%
Other values (5) 142
 
4.2%
Han
ValueCountFrequency (%)
1758
16.8%
1758
16.8%
1758
16.8%
1263
12.0%
946
9.0%
790
7.5%
790
7.5%
广 473
 
4.5%
473
 
4.5%
473
 
4.5%
Common
ValueCountFrequency (%)
' 106554
47.7%
[ 45426
20.3%
] 45426
20.3%
13078
 
5.9%
, 11665
 
5.2%
/ 1015
 
0.5%
? 50
 
< 0.1%
9 26
 
< 0.1%
\ 26
 
< 0.1%
Hebrew
ValueCountFrequency (%)
ִ 430
25.0%
י 215
12.5%
ע 215
12.5%
ב 215
12.5%
ְ 215
12.5%
ר 215
12.5%
ת 215
12.5%
Greek
ValueCountFrequency (%)
λ 426
25.0%
κ 213
12.5%
ε 213
12.5%
η 213
12.5%
ν 213
12.5%
ι 213
12.5%
ά 213
12.5%
Georgian
ValueCountFrequency (%)
33
14.3%
33
14.3%
33
14.3%
33
14.3%
33
14.3%
33
14.3%
33
14.3%
Devanagari
ValueCountFrequency (%)
706
16.7%
ि 706
16.7%
706
16.7%
706
16.7%
706
16.7%
706
16.7%
Hangul
ValueCountFrequency (%)
542
16.7%
542
16.7%
542
16.7%
542
16.7%
542
16.7%
542
16.7%
Thai
ValueCountFrequency (%)
350
28.6%
175
14.3%
175
14.3%
175
14.3%
175
14.3%
175
14.3%
Gurmukhi
ValueCountFrequency (%)
18
16.7%
18
16.7%
18
16.7%
18
16.7%
18
16.7%
18
16.7%
Telugu
ValueCountFrequency (%)
136
33.3%
68
16.7%
68
16.7%
68
16.7%
68
16.7%
Tamil
ValueCountFrequency (%)
111
20.0%
ி 111
20.0%
111
20.0%
111
20.0%
111
20.0%
Bengali
ValueCountFrequency (%)
94
40.0%
47
20.0%
47
20.0%
47
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 540587
92.0%
CJK 10482
 
1.8%
Cyrillic 10460
 
1.8%
None 10410
 
1.8%
Devanagari 4236
 
0.7%
Arabic 3349
 
0.6%
Hangul 3252
 
0.6%
Hebrew 1720
 
0.3%
Thai 1225
 
0.2%
Tamil 555
 
0.1%
Other values (6) 1108
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 106554
19.7%
[ 45426
8.4%
] 45426
8.4%
s 42269
 
7.8%
n 37466
 
6.9%
i 37113
 
6.9%
l 34638
 
6.4%
h 31462
 
5.8%
E 31203
 
5.8%
g 30418
 
5.6%
Other values (44) 98612
18.2%
None
ValueCountFrequency (%)
ç 4441
42.7%
ñ 2413
23.2%
ê 591
 
5.7%
λ 426
 
4.1%
Č 284
 
2.7%
ý 284
 
2.7%
ü 247
 
2.4%
κ 213
 
2.0%
ε 213
 
2.0%
η 213
 
2.0%
Other values (10) 1085
 
10.4%
Cyrillic
ValueCountFrequency (%)
с 3213
30.7%
к 1735
16.6%
и 1680
16.1%
й 1616
15.4%
у 1565
15.0%
а 113
 
1.1%
р 87
 
0.8%
ь 53
 
0.5%
н 53
 
0.5%
ї 53
 
0.5%
Other values (12) 292
 
2.8%
CJK
ValueCountFrequency (%)
1758
16.8%
1758
16.8%
1758
16.8%
1263
12.0%
946
9.0%
790
7.5%
790
7.5%
广 473
 
4.5%
473
 
4.5%
473
 
4.5%
Devanagari
ValueCountFrequency (%)
706
16.7%
ि 706
16.7%
706
16.7%
706
16.7%
706
16.7%
706
16.7%
Hangul
ValueCountFrequency (%)
542
16.7%
542
16.7%
542
16.7%
542
16.7%
542
16.7%
542
16.7%
Arabic
ValueCountFrequency (%)
ا 538
16.1%
ر 538
16.1%
ب 341
10.2%
ة 341
10.2%
ي 341
10.2%
ع 341
10.2%
ل 341
10.2%
ی 142
 
4.2%
ف 142
 
4.2%
س 142
 
4.2%
Other values (5) 142
 
4.2%
Hebrew
ValueCountFrequency (%)
ִ 430
25.0%
י 215
12.5%
ע 215
12.5%
ב 215
12.5%
ְ 215
12.5%
ר 215
12.5%
ת 215
12.5%
Thai
ValueCountFrequency (%)
350
28.6%
175
14.3%
175
14.3%
175
14.3%
175
14.3%
175
14.3%
Telugu
ValueCountFrequency (%)
136
33.3%
68
16.7%
68
16.7%
68
16.7%
68
16.7%
Tamil
ValueCountFrequency (%)
111
20.0%
ி 111
20.0%
111
20.0%
111
20.0%
111
20.0%
Bengali
ValueCountFrequency (%)
94
40.0%
47
20.0%
47
20.0%
47
20.0%
Latin Ext Additional
ValueCountFrequency (%)
ế 61
50.0%
61
50.0%
Georgian
ValueCountFrequency (%)
33
14.3%
33
14.3%
33
14.3%
33
14.3%
33
14.3%
33
14.3%
33
14.3%
Gurmukhi
ValueCountFrequency (%)
18
16.7%
18
16.7%
18
16.7%
18
16.7%
18
16.7%
18
16.7%
IPA Ext
ValueCountFrequency (%)
ə 4
100.0%

release_year
Real number (ℝ)

Distinct137
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1991.899
Minimum1874
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size709.8 KiB
2023-06-24T16:31:31.111662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1874
5-th percentile1941
Q11978
median2001
Q32010
95-th percentile2015
Maximum2021
Range147
Interquartile range (IQR)32

Descriptive statistics

Standard deviation24.046669
Coefficient of variation (CV)0.012072233
Kurtosis0.84383534
Mean1991.899
Median Absolute Deviation (MAD)12
Skewness-1.2259255
Sum90484004
Variance578.2423
MonotonicityNot monotonic
2023-06-24T16:31:31.371515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 1977
 
4.4%
2015 1908
 
4.2%
2013 1892
 
4.2%
2012 1729
 
3.8%
2011 1668
 
3.7%
2016 1609
 
3.5%
2009 1586
 
3.5%
2010 1503
 
3.3%
2008 1471
 
3.2%
2007 1323
 
2.9%
Other values (127) 28760
63.3%
ValueCountFrequency (%)
1874 1
 
< 0.1%
1878 1
 
< 0.1%
1883 1
 
< 0.1%
1887 1
 
< 0.1%
1888 2
 
< 0.1%
1890 5
 
< 0.1%
1891 6
< 0.1%
1892 3
 
< 0.1%
1893 1
 
< 0.1%
1894 13
< 0.1%
ValueCountFrequency (%)
2021 1
 
< 0.1%
2020 2
 
< 0.1%
2019 1
 
< 0.1%
2018 6
 
< 0.1%
2017 531
 
1.2%
2016 1609
3.5%
2015 1908
4.2%
2014 1977
4.4%
2013 1892
4.2%
2012 1729
3.8%

return
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct5232
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean659.31628
Minimum0
Maximum12396383
Zeros40045
Zeros (%)88.2%
Negative0
Negative (%)0.0%
Memory size709.8 KiB
2023-06-24T16:31:31.655011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.5333333
Maximum12396383
Range12396383
Interquartile range (IQR)0

Descriptive statistics

Standard deviation74652.178
Coefficient of variation (CV)113.22666
Kurtosis20695.739
Mean659.31628
Median Absolute Deviation (MAD)0
Skewness138.40572
Sum29950101
Variance5.5729477 × 109
MonotonicityNot monotonic
2023-06-24T16:31:31.934850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 40045
88.2%
1 20
 
< 0.1%
2 12
 
< 0.1%
4 11
 
< 0.1%
5 8
 
< 0.1%
3 7
 
< 0.1%
2.5 7
 
< 0.1%
1.333333333 7
 
< 0.1%
1.5 6
 
< 0.1%
7 4
 
< 0.1%
Other values (5222) 5299
 
11.7%
ValueCountFrequency (%)
0 40045
88.2%
5.217391304 × 10-71
 
< 0.1%
7.5 × 10-71
 
< 0.1%
9.375 × 10-71
 
< 0.1%
1.499133126 × 10-61
 
< 0.1%
1.8 × 10-61
 
< 0.1%
1.916666667 × 10-61
 
< 0.1%
3.5 × 10-61
 
< 0.1%
4 × 10-61
 
< 0.1%
5.111111111 × 10-61
 
< 0.1%
ValueCountFrequency (%)
12396383 1
< 0.1%
8500000 1
< 0.1%
4197476.625 1
< 0.1%
2755584 1
< 0.1%
1018619.283 1
< 0.1%
1000000 1
< 0.1%
26881.72043 1
< 0.1%
12890.38667 1
< 0.1%
5330.33945 1
< 0.1%
4133.333333 1
< 0.1%

names_cast
Text

MISSING 

Distinct42672
Distinct (%)99.2%
Missing2399
Missing (%)5.3%
Memory size709.8 KiB
2023-06-24T16:31:32.499508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4551
Median length1364
Mean length198.0135
Min length4

Characters and Unicode

Total characters8519927
Distinct characters395
Distinct categories16 ?
Distinct scripts9 ?
Distinct blocks10 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42492 ?
Unique (%)98.8%

Sample

1st rowTom Hanks, Tim Allen, Don Rickles, Jim Varney, Wallace Shawn, John Ratzenberger, Annie Potts, John Morris, Erik von Detten, Laurie Metcalf, R. Lee Ermey, Sarah Freeman, Penn Jillette
2nd rowRobin Williams, Jonathan Hyde, Kirsten Dunst, Bradley Pierce, Bonnie Hunt, Bebe Neuwirth, David Alan Grier, Patricia Clarkson, Adam Hann-Byrd, Laura Bell Bundy, James Handy, Gillian Barber, Brandon Obray, Cyrus Thiedeke, Gary Joseph Thorup, Leonard Zola, Lloyd Berry, Malcolm Stewart, Annabel Kershaw, Darryl Henriques, Robyn Driscoll, Peter Bryant, Sarah Gilson, Florica Vlad, June Lion, Brenda Lockmuller
3rd rowWalter Matthau, Jack Lemmon, Ann-Margret, Sophia Loren, Daryl Hannah, Burgess Meredith, Kevin Pollak
4th rowWhitney Houston, Angela Bassett, Loretta Devine, Lela Rochon, Gregory Hines, Dennis Haysbert, Michael Beach, Mykelti Williamson, Lamont Johnson, Wesley Snipes
5th rowSteve Martin, Diane Keaton, Martin Short, Kimberly Williams-Paisley, George Newbern, Kieran Culkin, BD Wong, Peter Michael Goetz, Kate McGregor-Stewart, Jane Adams, Eugene Levy, Lori Alan
ValueCountFrequency (%)
john 9806
 
0.8%
michael 7457
 
0.6%
david 6184
 
0.5%
robert 5721
 
0.5%
james 5692
 
0.5%
richard 4445
 
0.4%
paul 4316
 
0.4%
peter 3901
 
0.3%
william 3431
 
0.3%
george 3415
 
0.3%
Other values (112948) 1110597
95.3%
2023-06-24T16:31:33.351383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1122066
 
13.2%
a 704895
 
8.3%
e 665257
 
7.8%
n 524096
 
6.2%
, 519445
 
6.1%
r 497334
 
5.8%
i 483975
 
5.7%
o 423789
 
5.0%
l 366443
 
4.3%
s 255864
 
3.0%
Other values (385) 2956763
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5650804
66.3%
Uppercase Letter 1190381
 
14.0%
Space Separator 1122069
 
13.2%
Other Punctuation 541747
 
6.4%
Dash Punctuation 14102
 
0.2%
Other Letter 543
 
< 0.1%
Decimal Number 94
 
< 0.1%
Final Punctuation 83
 
< 0.1%
Initial Punctuation 23
 
< 0.1%
Open Punctuation 23
 
< 0.1%
Other values (6) 58
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 704895
12.5%
e 665257
11.8%
n 524096
9.3%
r 497334
 
8.8%
i 483975
 
8.6%
o 423789
 
7.5%
l 366443
 
6.5%
s 255864
 
4.5%
t 253207
 
4.5%
h 197893
 
3.5%
Other values (138) 1278051
22.6%
Other Letter
ValueCountFrequency (%)
ا 32
 
5.9%
م 31
 
5.7%
ی 19
 
3.5%
ع 19
 
3.5%
ن 18
 
3.3%
17
 
3.1%
د 17
 
3.1%
ر 17
 
3.1%
ي 16
 
2.9%
12
 
2.2%
Other values (104) 345
63.5%
Uppercase Letter
ValueCountFrequency (%)
M 109335
 
9.2%
S 92324
 
7.8%
C 84005
 
7.1%
J 83325
 
7.0%
B 82367
 
6.9%
A 70817
 
5.9%
R 67383
 
5.7%
D 65886
 
5.5%
L 61157
 
5.1%
G 54661
 
4.6%
Other values (81) 419121
35.2%
Decimal Number
ValueCountFrequency (%)
5 37
39.4%
0 29
30.9%
1 8
 
8.5%
2 8
 
8.5%
9 4
 
4.3%
3 2
 
2.1%
7 2
 
2.1%
4 2
 
2.1%
8 1
 
1.1%
6 1
 
1.1%
Other Punctuation
ValueCountFrequency (%)
, 519445
95.9%
. 16049
 
3.0%
' 6097
 
1.1%
" 129
 
< 0.1%
· 9
 
< 0.1%
: 6
 
< 0.1%
& 6
 
< 0.1%
! 5
 
< 0.1%
/ 1
 
< 0.1%
Nonspacing Mark
ValueCountFrequency (%)
́ 10
58.8%
2
 
11.8%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
Final Punctuation
ValueCountFrequency (%)
74
89.2%
6
 
7.2%
» 3
 
3.6%
Space Separator
ValueCountFrequency (%)
1122066
> 99.9%
  3
 
< 0.1%
Initial Punctuation
ValueCountFrequency (%)
20
87.0%
« 3
 
13.0%
Open Punctuation
ValueCountFrequency (%)
14
60.9%
( 9
39.1%
Format
ValueCountFrequency (%)
5
83.3%
1
 
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 14102
100.0%
Control
ValueCountFrequency (%)
21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 3
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6838101
80.3%
Common 1678181
 
19.7%
Cyrillic 3070
 
< 0.1%
Han 276
 
< 0.1%
Arabic 241
 
< 0.1%
Thai 27
 
< 0.1%
Greek 14
 
< 0.1%
Inherited 11
 
< 0.1%
Hangul 6
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 704895
 
10.3%
e 665257
 
9.7%
n 524096
 
7.7%
r 497334
 
7.3%
i 483975
 
7.1%
o 423789
 
6.2%
l 366443
 
5.4%
s 255864
 
3.7%
t 253207
 
3.7%
h 197893
 
2.9%
Other values (163) 2465348
36.1%
Han
ValueCountFrequency (%)
17
 
6.2%
12
 
4.3%
11
 
4.0%
11
 
4.0%
11
 
4.0%
11
 
4.0%
11
 
4.0%
11
 
4.0%
9
 
3.3%
9
 
3.3%
Other values (55) 163
59.1%
Cyrillic
ValueCountFrequency (%)
а 323
 
10.5%
и 315
 
10.3%
о 233
 
7.6%
н 229
 
7.5%
р 215
 
7.0%
е 174
 
5.7%
л 155
 
5.0%
к 136
 
4.4%
т 115
 
3.7%
с 109
 
3.6%
Other values (51) 1066
34.7%
Common
ValueCountFrequency (%)
1122066
66.9%
, 519445
31.0%
. 16049
 
1.0%
- 14102
 
0.8%
' 6097
 
0.4%
" 129
 
< 0.1%
74
 
< 0.1%
5 37
 
< 0.1%
0 29
 
< 0.1%
21
 
< 0.1%
Other values (24) 132
 
< 0.1%
Arabic
ValueCountFrequency (%)
ا 32
13.3%
م 31
12.9%
ی 19
 
7.9%
ع 19
 
7.9%
ن 18
 
7.5%
د 17
 
7.1%
ر 17
 
7.1%
ي 16
 
6.6%
ل 9
 
3.7%
ب 8
 
3.3%
Other values (18) 55
22.8%
Thai
ValueCountFrequency (%)
2
 
7.4%
2
 
7.4%
2
 
7.4%
2
 
7.4%
2
 
7.4%
2
 
7.4%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
Other values (11) 11
40.7%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Greek
ValueCountFrequency (%)
ν 6
42.9%
Ζ 2
 
14.3%
α 2
 
14.3%
ί 2
 
14.3%
ο 2
 
14.3%
Inherited
ValueCountFrequency (%)
́ 10
90.9%
1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8477864
99.5%
None 38257
 
0.4%
Cyrillic 3070
 
< 0.1%
CJK 276
 
< 0.1%
Arabic 241
 
< 0.1%
Punctuation 120
 
< 0.1%
Latin Ext Additional 56
 
< 0.1%
Thai 27
 
< 0.1%
Diacriticals 10
 
< 0.1%
Hangul 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1122066
 
13.2%
a 704895
 
8.3%
e 665257
 
7.8%
n 524096
 
6.2%
, 519445
 
6.1%
r 497334
 
5.9%
i 483975
 
5.7%
o 423789
 
5.0%
l 366443
 
4.3%
s 255864
 
3.0%
Other values (66) 2914700
34.4%
None
ValueCountFrequency (%)
é 9077
23.7%
á 4155
 
10.9%
í 2756
 
7.2%
ô 2331
 
6.1%
ö 2014
 
5.3%
ó 1882
 
4.9%
ü 1492
 
3.9%
ć 1360
 
3.6%
è 1243
 
3.2%
ä 994
 
2.6%
Other values (111) 10953
28.6%
Cyrillic
ValueCountFrequency (%)
а 323
 
10.5%
и 315
 
10.3%
о 233
 
7.6%
н 229
 
7.5%
р 215
 
7.0%
е 174
 
5.7%
л 155
 
5.0%
к 136
 
4.4%
т 115
 
3.7%
с 109
 
3.6%
Other values (51) 1066
34.7%
Punctuation
ValueCountFrequency (%)
74
61.7%
20
 
16.7%
14
 
11.7%
6
 
5.0%
5
 
4.2%
1
 
0.8%
Arabic
ValueCountFrequency (%)
ا 32
13.3%
م 31
12.9%
ی 19
 
7.9%
ع 19
 
7.9%
ن 18
 
7.5%
د 17
 
7.1%
ر 17
 
7.1%
ي 16
 
6.6%
ل 9
 
3.7%
ب 8
 
3.3%
Other values (18) 55
22.8%
CJK
ValueCountFrequency (%)
17
 
6.2%
12
 
4.3%
11
 
4.0%
11
 
4.0%
11
 
4.0%
11
 
4.0%
11
 
4.0%
11
 
4.0%
9
 
3.3%
9
 
3.3%
Other values (55) 163
59.1%
Latin Ext Additional
ValueCountFrequency (%)
15
26.8%
9
16.1%
6
 
10.7%
6
 
10.7%
ế 5
 
8.9%
4
 
7.1%
4
 
7.1%
4
 
7.1%
2
 
3.6%
1
 
1.8%
Diacriticals
ValueCountFrequency (%)
́ 10
100.0%
Thai
ValueCountFrequency (%)
2
 
7.4%
2
 
7.4%
2
 
7.4%
2
 
7.4%
2
 
7.4%
2
 
7.4%
1
 
3.7%
1
 
3.7%
1
 
3.7%
1
 
3.7%
Other values (11) 11
40.7%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

director
Text

MISSING 

Distinct17570
Distinct (%)39.4%
Missing874
Missing (%)1.9%
Memory size709.8 KiB
2023-06-24T16:31:33.844121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length33
Median length29
Mean length13.465838
Min length2

Characters and Unicode

Total characters599930
Distinct characters200
Distinct categories10 ?
Distinct scripts6 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10624 ?
Unique (%)23.8%

Sample

1st rowJohn Lasseter
2nd rowJoe Johnston
3rd rowHoward Deutch
4th rowForest Whitaker
5th rowCharles Shyer
ValueCountFrequency (%)
john 1164
 
1.2%
michael 878
 
0.9%
robert 806
 
0.9%
david 806
 
0.9%
peter 524
 
0.6%
william 513
 
0.5%
richard 511
 
0.5%
james 489
 
0.5%
paul 439
 
0.5%
george 398
 
0.4%
Other values (17095) 87132
93.0%
2023-06-24T16:31:34.602781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 52290
 
8.7%
a 51671
 
8.6%
49210
 
8.2%
r 40766
 
6.8%
n 40228
 
6.7%
i 38975
 
6.5%
o 35342
 
5.9%
l 27454
 
4.6%
s 20776
 
3.5%
t 19747
 
3.3%
Other values (190) 223471
37.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 451006
75.2%
Uppercase Letter 95330
 
15.9%
Space Separator 49210
 
8.2%
Other Punctuation 3104
 
0.5%
Dash Punctuation 1237
 
0.2%
Other Letter 21
 
< 0.1%
Control 12
 
< 0.1%
Decimal Number 6
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 52290
11.6%
a 51671
11.5%
r 40766
 
9.0%
n 40228
 
8.9%
i 38975
 
8.6%
o 35342
 
7.8%
l 27454
 
6.1%
s 20776
 
4.6%
t 19747
 
4.4%
h 16694
 
3.7%
Other values (97) 107063
23.7%
Uppercase Letter
ValueCountFrequency (%)
M 8349
 
8.8%
S 7925
 
8.3%
J 7203
 
7.6%
R 6164
 
6.5%
B 5972
 
6.3%
C 5953
 
6.2%
A 5710
 
6.0%
D 5101
 
5.4%
L 4947
 
5.2%
G 4563
 
4.8%
Other values (52) 33443
35.1%
Other Letter
ValueCountFrequency (%)
م 2
 
9.5%
ا 2
 
9.5%
ی 2
 
9.5%
ع 1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
پ 1
 
4.8%
ن 1
 
4.8%
Other values (8) 8
38.1%
Other Punctuation
ValueCountFrequency (%)
. 2883
92.9%
' 206
 
6.6%
, 14
 
0.5%
· 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 3
50.0%
5 1
 
16.7%
3 1
 
16.7%
9 1
 
16.7%
Space Separator
ValueCountFrequency (%)
49210
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1237
100.0%
Control
ValueCountFrequency (%)
12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 546192
91.0%
Common 53573
 
8.9%
Cyrillic 144
 
< 0.1%
Arabic 10
 
< 0.1%
Han 8
 
< 0.1%
Hangul 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 52290
 
9.6%
a 51671
 
9.5%
r 40766
 
7.5%
n 40228
 
7.4%
i 38975
 
7.1%
o 35342
 
6.5%
l 27454
 
5.0%
s 20776
 
3.8%
t 19747
 
3.6%
h 16694
 
3.1%
Other values (123) 202249
37.0%
Cyrillic
ValueCountFrequency (%)
и 19
13.2%
е 11
 
7.6%
л 11
 
7.6%
о 11
 
7.6%
р 10
 
6.9%
а 10
 
6.9%
к 8
 
5.6%
н 7
 
4.9%
д 6
 
4.2%
в 6
 
4.2%
Other values (26) 45
31.2%
Common
ValueCountFrequency (%)
49210
91.9%
. 2883
 
5.4%
- 1237
 
2.3%
' 206
 
0.4%
, 14
 
< 0.1%
12
 
< 0.1%
0 3
 
< 0.1%
) 2
 
< 0.1%
( 2
 
< 0.1%
5 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
Han
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Arabic
ValueCountFrequency (%)
م 2
20.0%
ا 2
20.0%
ی 2
20.0%
ع 1
10.0%
پ 1
10.0%
ن 1
10.0%
د 1
10.0%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 595877
99.3%
None 3885
 
0.6%
Cyrillic 144
 
< 0.1%
Arabic 10
 
< 0.1%
CJK 8
 
< 0.1%
Latin Ext Additional 3
 
< 0.1%
Hangul 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 52290
 
8.8%
a 51671
 
8.7%
49210
 
8.3%
r 40766
 
6.8%
n 40228
 
6.8%
i 38975
 
6.5%
o 35342
 
5.9%
l 27454
 
4.6%
s 20776
 
3.5%
t 19747
 
3.3%
Other values (54) 219418
36.8%
None
ValueCountFrequency (%)
é 916
23.6%
á 379
 
9.8%
ö 255
 
6.6%
ó 229
 
5.9%
í 228
 
5.9%
ô 153
 
3.9%
ä 149
 
3.8%
è 134
 
3.4%
ü 108
 
2.8%
ç 106
 
2.7%
Other values (69) 1228
31.6%
Cyrillic
ValueCountFrequency (%)
и 19
13.2%
е 11
 
7.6%
л 11
 
7.6%
о 11
 
7.6%
р 10
 
6.9%
а 10
 
6.9%
к 8
 
5.6%
н 7
 
4.9%
д 6
 
4.2%
в 6
 
4.2%
Other values (26) 45
31.2%
Arabic
ValueCountFrequency (%)
م 2
20.0%
ا 2
20.0%
ی 2
20.0%
ع 1
10.0%
پ 1
10.0%
ن 1
10.0%
د 1
10.0%
Latin Ext Additional
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
CJK
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

ganancia
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7740
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6989699.4
Minimum-1.6571009 × 108
Maximum2.5509651 × 109
Zeros34533
Zeros (%)76.0%
Negative5113
Negative (%)11.3%
Memory size709.8 KiB
2023-06-24T16:31:34.889614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.6571009 × 108
5-th percentile-4828431.5
Q10
median0
Q30
95-th percentile28023563
Maximum2.5509651 × 109
Range2.7166752 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation52160415
Coefficient of variation (CV)7.4624691
Kurtosis321.22839
Mean6989699.4
Median Absolute Deviation (MAD)0
Skewness13.95675
Sum3.1751408 × 1011
Variance2.7207089 × 1015
MonotonicityNot monotonic
2023-06-24T16:31:35.167459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34533
76.0%
-2000000 154
 
0.3%
-5000000 146
 
0.3%
-1000000 137
 
0.3%
-3000000 130
 
0.3%
-10000000 113
 
0.2%
-500000 110
 
0.2%
-4000000 88
 
0.2%
-1500000 83
 
0.2%
-8000000 74
 
0.2%
Other values (7730) 9858
 
21.7%
ValueCountFrequency (%)
-165710090 1
 
< 0.1%
-150000000 2
< 0.1%
-125000000 1
 
< 0.1%
-120000000 1
 
< 0.1%
-119180039 1
 
< 0.1%
-111007242 1
 
< 0.1%
-107447384 1
 
< 0.1%
-104002432 1
 
< 0.1%
-100000000 4
< 0.1%
-98301101 1
 
< 0.1%
ValueCountFrequency (%)
2550965087 1
< 0.1%
1823223624 1
< 0.1%
1645034188 1
< 0.1%
1363528810 1
< 0.1%
1316249360 1
< 0.1%
1299557910 1
< 0.1%
1217000000 1
< 0.1%
1125403694 1
< 0.1%
1124219009 1
< 0.1%
1102886337 1
< 0.1%

Interactions

2023-06-24T16:31:02.627087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:36.587147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:39.403685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:41.863166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:44.408520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:47.138436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:49.673053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:52.178705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:54.822247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:57.530794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:00.120548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:02.868946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:36.841169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:39.637776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:42.109327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:44.642764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:47.392614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:49.911550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:52.434384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:55.060427image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:57.778670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:00.362406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:03.081842image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:37.063643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:39.845317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:42.325500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:44.855438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:47.610365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:50.127168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:52.658089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:55.277087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:58.000525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:00.589295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:03.298884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:37.295745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:40.068001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:42.542997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:45.104909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:47.838979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:50.342944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:52.893360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:55.492963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:58.225397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:00.809808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:03.515741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:37.539026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:40.290483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:42.788759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:45.325775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:48.074781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:50.568826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:53.136412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:55.723832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:58.455263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:01.030935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:03.808954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:37.945664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:40.513127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:43.014503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:45.552386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:48.296945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:50.790435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:53.373547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:56.205572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:58.703121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:01.260804image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:04.108800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:38.180295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:40.731669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:43.245190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:45.985588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:48.513417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:51.009150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:53.609954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:56.419454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:58.930991image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:01.478694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:04.344649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:38.430934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:40.953588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:43.478237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:46.214859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:48.752719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:51.243606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:53.852617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:56.639305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:59.172092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:01.714611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:04.559542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:38.675839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:41.173524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:43.707742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:46.443531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:48.975053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:51.474904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:54.087031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:56.852187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:59.393968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:01.929484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:04.798392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:38.919285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:41.411749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:43.946284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:46.685177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:49.213280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:51.721917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:54.338129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:57.076072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:59.635827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:02.175348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:05.032271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:39.164190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:41.642382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:44.184959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:46.921838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:49.444861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:51.956299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:54.579126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:57.293929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:30:59.877686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-24T16:31:02.406214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-24T16:31:35.393330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
budgetidpopularityrevenueruntimevote_averagevote_countid_collectionrelease_yearreturngananciastatus
budget1.000-0.2560.4630.6440.2270.0720.484-0.2990.1410.775-0.1090.000
id-0.2561.000-0.412-0.278-0.207-0.150-0.4350.4280.392-0.262-0.0970.056
popularity0.463-0.4121.0000.4910.3080.2430.894-0.3470.1840.4470.1520.000
revenue0.644-0.2780.4911.0000.2540.1270.513-0.3260.1030.8530.5170.000
runtime0.227-0.2070.3080.2541.0000.1940.291-0.1360.0330.2340.0910.000
vote_average0.072-0.1500.2430.1270.1941.0000.319-0.012-0.0090.1200.1110.018
vote_count0.484-0.4350.8940.5130.2910.3191.000-0.3630.1950.4740.1670.000
id_collection-0.2990.428-0.347-0.326-0.136-0.012-0.3631.0000.040-0.315-0.2620.000
release_year0.1410.3920.1840.1030.033-0.0090.1950.0401.0000.086-0.0190.030
return0.775-0.2620.4470.8530.2340.1200.474-0.3150.0861.0000.3400.000
ganancia-0.109-0.0970.1520.5170.0910.1110.167-0.262-0.0190.3401.0000.000
status0.0000.0560.0000.0000.0000.0180.0000.0000.0300.0000.0001.000

Missing values

2023-06-24T16:31:05.463004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-24T16:31:06.452001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-24T16:31:07.397458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

budgetidoriginal_languageoverviewpopularityrelease_daterevenueruntimestatustaglinetitlevote_averagevote_countid_collectionname_collectionposter_path_collectionbackdrop_path_collectionname_companyid_companygenres_namegenres_idproduction_countries_iso_3166_1production_countries_namesspoken_languages_iso_639_1spoken_languages_namerelease_yearreturnnames_castdirectorganancia
030000000862enLed by Woody, Andy's toys live happily in his room until Andy's birthday brings Buzz Lightyear onto the scene. Afraid of losing his place in Andy's heart, Woody plots against Buzz. But when circumstances separate Buzz and Woody from their owner, the duo eventually learns to put aside their differences.21.9469431995-10-3037355403381.0ReleasedNaNToy Story7.75415.010194.0Toy Story Collection/7G9915LfUQ2lVfwMEEhDsn3kT4B.jpg/9FBwqcd9IRruEDUrTdcaafOMKUq.jpgPixar Animation Studios3['Animation', 'Comedy', 'Family'][16, 35, 10751]['US']['United States of America']['en']['English']199512.451801Tom Hanks, Tim Allen, Don Rickles, Jim Varney, Wallace Shawn, John Ratzenberger, Annie Potts, John Morris, Erik von Detten, Laurie Metcalf, R. Lee Ermey, Sarah Freeman, Penn JilletteJohn Lasseter343554033
1650000008844enWhen siblings Judy and Peter discover an enchanted board game that opens the door to a magical world, they unwittingly invite Alan -- an adult who's been trapped inside the game for 26 years -- into their living room. Alan's only hope for freedom is to finish the game, which proves risky as all three find themselves running from giant rhinoceroses, evil monkeys and other terrifying creatures.17.0155391995-12-15262797249104.0ReleasedRoll the dice and unleash the excitement!Jumanji6.92413.0NaNNaNNaNNaNTriStar Pictures, Teitler Film, Interscope Communications559, 2550, 10201['Adventure', 'Fantasy', 'Family'][12, 14, 10751]['US']['United States of America']['en', 'fr']['English', 'Français']19954.043035Robin Williams, Jonathan Hyde, Kirsten Dunst, Bradley Pierce, Bonnie Hunt, Bebe Neuwirth, David Alan Grier, Patricia Clarkson, Adam Hann-Byrd, Laura Bell Bundy, James Handy, Gillian Barber, Brandon Obray, Cyrus Thiedeke, Gary Joseph Thorup, Leonard Zola, Lloyd Berry, Malcolm Stewart, Annabel Kershaw, Darryl Henriques, Robyn Driscoll, Peter Bryant, Sarah Gilson, Florica Vlad, June Lion, Brenda LockmullerJoe Johnston197797249
2015602enA family wedding reignites the ancient feud between next-door neighbors and fishing buddies John and Max. Meanwhile, a sultry Italian divorcée opens a restaurant at the local bait shop, alarming the locals who worry she'll scare the fish away. But she's less interested in seafood than she is in cooking up a hot time with Max.11.7129001995-12-220101.0ReleasedStill Yelling. Still Fighting. Still Ready for Love.Grumpier Old Men6.592.0119050.0Grumpy Old Men Collection/nLvUdqgPgm3F85NMCii9gVFUcet.jpg/hypTnLot2z8wpFS7qwsQHW1uV8u.jpgWarner Bros., Lancaster Gate6194, 19464['Romance', 'Comedy'][10749, 35]['US']['United States of America']['en']['English']19950.000000Walter Matthau, Jack Lemmon, Ann-Margret, Sophia Loren, Daryl Hannah, Burgess Meredith, Kevin PollakHoward Deutch0
31600000031357enCheated on, mistreated and stepped on, the women are holding their breath, waiting for the elusive "good man" to break a string of less-than-stellar lovers. Friends and confidants Vannah, Bernie, Glo and Robin talk it all out, determined to find a better way to breathe.3.8594951995-12-2281452156127.0ReleasedFriends are the people who let you be yourself... and never let you forget it.Waiting to Exhale6.134.0NaNNaNNaNNaNTwentieth Century Fox Film Corporation306['Comedy', 'Drama', 'Romance'][35, 18, 10749]['US']['United States of America']['en']['English']19955.090760Whitney Houston, Angela Bassett, Loretta Devine, Lela Rochon, Gregory Hines, Dennis Haysbert, Michael Beach, Mykelti Williamson, Lamont Johnson, Wesley SnipesForest Whitaker65452156
4011862enJust when George Banks has recovered from his daughter's wedding, he receives the news that she's pregnant ... and that George's wife, Nina, is expecting too. He was planning on selling their home, but that's a plan that -- like George -- will have to change with the arrival of both a grandchild and a kid of his own.8.3875191995-02-1076578911106.0ReleasedJust When His World Is Back To Normal... He's In For The Surprise Of His Life!Father of the Bride Part II5.7173.096871.0Father of the Bride Collection/nts4iOmNnq7GNicycMJ9pSAn204.jpg/7qwE57OVZmMJChBpLEbJEmzUydk.jpgSandollar Productions, Touchstone Pictures5842, 9195['Comedy'][35]['US']['United States of America']['en']['English']19950.000000Steve Martin, Diane Keaton, Martin Short, Kimberly Williams-Paisley, George Newbern, Kieran Culkin, BD Wong, Peter Michael Goetz, Kate McGregor-Stewart, Jane Adams, Eugene Levy, Lori AlanCharles Shyer76578911
560000000949enObsessive master thief, Neil McCauley leads a top-notch crew on various insane heists throughout Los Angeles while a mentally unstable detective, Vincent Hanna pursues him without rest. Each man recognizes and respects the ability and the dedication of the other even though they are aware their cat-and-mouse game may end in violence.17.9249271995-12-15187436818170.0ReleasedA Los Angeles Crime SagaHeat7.71886.0NaNNaNNaNNaNRegency Enterprises, Forward Pass, Warner Bros.508, 675, 6194['Action', 'Crime', 'Drama', 'Thriller'][28, 80, 18, 53]['US']['United States of America']['en', 'es']['English', 'Español']19953.123947Al Pacino, Robert De Niro, Val Kilmer, Jon Voight, Tom Sizemore, Diane Venora, Amy Brenneman, Ashley Judd, Mykelti Williamson, Natalie Portman, Ted Levine, Tom Noonan, Tone Loc, Hank Azaria, Wes Studi, Dennis Haysbert, Danny Trejo, Henry Rollins, William Fichtner, Kevin Gage, Susan Traylor, Jerry Trimble, Ricky Harris, Jeremy Piven, Xander Berkeley, Begonya Plaza, Rick Avery, Hazelle Goodman, Ray Buktenica, Max Daniels, Vince Deadrick Jr., Steven Ford, Farrah Forke, Patricia Healy, Paul Herman, Cindy Katz, Brian Libby, Dan Martin, Mario Roberts, Thomas Rosales, Jr., Yvonne Zima, Mick Gould, Bud Cort, Viviane Vives, Kim Staunton, Martin Ferrero, Brad Baldridge, Andrew Camuccio, Kenny Endoso, Kimberly Flynn, Niki Harris, Bill McIntosh, Rick Marzan, Terry Miller, Daniel O'Haco, Kai Soremekun, Peter Blackwell, Trevor Coppola, Mary Kircher, Darin Mangan, Robert Miranda, Manny Perry, Iva Franks Singer, Tim Werner, Philip EttingtonMichael Mann127436818
65800000011860enAn ugly duckling having undergone a remarkable change, still harbors feelings for her crush: a carefree playboy, but not before his business-focused brother has something to say about it.6.6772771995-12-150127.0ReleasedYou are cordially invited to the most surprising merger of the year.Sabrina6.2141.0NaNNaNNaNNaNParamount Pictures, Scott Rudin Productions, Mirage Enterprises, Sandollar Productions, Constellation Entertainment, Worldwide, Mont Blanc Entertainment GmbH4, 258, 932, 5842, 14941, 55873, 58079['Comedy', 'Romance'][35, 10749]['DE', 'US']['Germany', 'United States of America']['fr', 'en']['Français', 'English']19950.000000Harrison Ford, Julia Ormond, Greg Kinnear, Angie Dickinson, Nancy Marchand, John Wood, Richard Crenna, Lauren Holly, Dana Ivey, Fanny Ardant, Patrick Bruel, Paul Giamatti, Miriam Colón, Elizabeth Franz, Valérie Lemercier, Becky Ann Baker, John C. Vennema, Margo Martindale, J. Smith-Cameron, Christine Luneau-Lipton, Michael Dees, Denis Holmes, Jo-Jo Lowe, Ira Wheeler, Philippa Cooper, Ayako Kawahara, François Genty, Guillaume Gallienne, Inés Sastre, Phina Oruche, Andrea Behalikova, Jennifer Herrera, Kristina Kumlin, Eva Linderholm, Carmen Chaplin, Micheline Van de Velde, Joanna Rhodes, Alan Boone, Patrick Forster-Delmas, Kentaro Matsuo, Peter McKernan, Ed Connelly, Ronald L. Schwary, Alvin Lum, Siching Song, Phil Nee, Randy Becker, Susan Browning, Anthony Mondal, Peter Parks, Woodrow Asai, Eric Bruno Borgman, Michael Cline, Christopher Del Gaudio, Philippe Hartmann, Jerry Quinn, Dori RosenthalSydney Pollack-58000000
7045325enA mischievous young boy, Tom Sawyer, witnesses a murder by the deadly Injun Joe. Tom becomes friends with Huckleberry Finn, a boy with no future and no family. Tom has to choose between honoring a friendship or honoring an oath because the town alcoholic is accused of the murder. Tom and Huck go through several adventures trying to retrieve evidence.2.5611611995-12-22097.0ReleasedThe Original Bad Boys.Tom and Huck5.445.0NaNNaNNaNNaNWalt Disney Pictures2['Action', 'Adventure', 'Drama', 'Family'][28, 12, 18, 10751]['US']['United States of America']['en', 'de']['English', 'Deutsch']19950.000000Jonathan Taylor Thomas, Brad Renfro, Rachael Leigh Cook, Michael McShane, Amy Wright, Eric Schweig, Tamara MelloPeter Hewitt0
8350000009091enInternational action superstar Jean Claude Van Damme teams with Powers Boothe in a Tension-packed, suspense thriller, set against the back-drop of a Stanley Cup game.Van Damme portrays a father whose daughter is suddenly taken during a championship hockey game. With the captors demanding a billion dollars by game's end, Van Damme frantically sets a plan in motion to rescue his daughter and abort an impending explosion before the final buzzer...5.2315801995-12-2264350171106.0ReleasedTerror goes into overtime.Sudden Death5.5174.0NaNNaNNaNNaNUniversal Pictures, Imperial Entertainment, Signature Entertainment33, 21437, 23770['Action', 'Adventure', 'Thriller'][28, 12, 53]['US']['United States of America']['en']['English']19951.838576Jean-Claude Van Damme, Powers Boothe, Dorian Harewood, Raymond J. Barry, Ross Malinger, Whittni WrightPeter Hyams29350171
958000000710enJames Bond must unmask the mysterious head of the Janus Syndicate and prevent the leader from utilizing the GoldenEye weapons system to inflict devastating revenge on Britain.14.6860361995-11-16352194034130.0ReleasedNo limits. No fears. No substitutes.GoldenEye6.61194.0645.0James Bond Collection/HORpg5CSkmeQlAolx3bKMrKgfi.jpg/6VcVl48kNKvdXOZfJPdarlUGOsk.jpgUnited Artists, Eon Productions60, 7576['Adventure', 'Action', 'Thriller'][12, 28, 53]['GB', 'US']['United Kingdom', 'United States of America']['en', 'ru', 'es']['English', 'Pусский', 'Español']19956.072311Pierce Brosnan, Sean Bean, Izabella Scorupco, Famke Janssen, Joe Don Baker, Judi Dench, Gottfried John, Robbie Coltrane, Alan Cumming, Tchéky Karyo, Desmond Llewelyn, Samantha Bond, Michael Kitchen, Serena Gordon, Simon Kunz, Billy J. Mitchell, Constantine Gregory, Minnie Driver, Michelle Arthur, Ravil IsyanovMartin Campbell294194034
budgetidoriginal_languageoverviewpopularityrelease_daterevenueruntimestatustaglinetitlevote_averagevote_countid_collectionname_collectionposter_path_collectionbackdrop_path_collectionname_companyid_companygenres_namegenres_idproduction_countries_iso_3166_1production_countries_namesspoken_languages_iso_639_1spoken_languages_namerelease_yearreturnnames_castdirectorganancia
45432084419enAn unsuccessful sculptor saves a madman named "The Creeper" from drowning. Seeing an opportunity for revenge, he tricks the psycho into murdering his critics.0.2228141946-03-29065.0ReleasedMeet...The CREEPER!House of Horrors6.38.0NaNNaNNaNNaNUniversal Pictures33['Horror', 'Mystery', 'Thriller'][27, 9648, 53]['US']['United States of America']['en']['English']19460.0Rondo Hatton, Robert Lowery, Virginia Grey, Bill Goodwin, Martin Kosleck, Alan Napier, Howard Freeman, Virginia Christine, Joan Shawlee, Byron Foulger, Syd SaylorJean Yarbrough0
454330390959enIn this true-crime documentary, we delve into the murder spree that was the inspiration for Joe Berlinger's "Book of Shadows: Blair Witch 2".0.0760612000-10-22045.0ReleasedNaNShadow of the Blair Witch7.02.0NaNNaNNaNNaNNaNNaN['Mystery', 'Horror'][9648, 27][][]['en']['English']20000.0Tony Abatemarco, Andre Brooks, Mariclare Costello, Bill Dreggors, Apollo Dukakis, Philip Friedman, James Gleason, Dilva Henry, Bari Hochwald, Wendy Hoffman, John Huck, Rachel Moskowitz, Sandy Mulvihill, Roger Nolan, Chris Parnell, Byrne Piven, Richard Sexton, Rich Williams, Ray XifoBen Rock0
454340289923enA film archivist revisits the story of Rustin Parr, a hermit thought to have murdered seven children while under the possession of the Blair Witch.0.3864502000-10-03030.0ReleasedDo you know what happened 50 years before "The Blair Witch Project"?The Burkittsville 77.01.0NaNNaNNaNNaNNeptune Salad Entertainment, Pirie Productions27570, 27571['Horror'][27]['US']['United States of America']['en']['English']20000.0Monty Bane, Lucy Butler, David Grammer, Bill Dreggors, Frank Pastor, Heather Donahue, Joshua Leonard, Michael C. WilliamsBen Rock0
454350222848enIt's the year 3000 AD. The world's most dangerous women are banished to a remote asteroid 45 million light years from earth. Kira Murphy doesn't belong; wrongfully accused of a crime she did not commit, she's thrown in this interplanetary prison and left to her own defenses. But Kira's a fighter, and soon she finds herself in the middle of a female gang war; where everyone wants a piece of the action... and a piece of her! "Caged Heat 3000" takes the Women-in-Prison genre to a whole new level... and a whole new galaxy!0.6615581995-01-01085.0ReleasedNaNCaged Heat 30003.51.0NaNNaNNaNNaNConcorde-New Horizons4688['Science Fiction'][878]['US']['United States of America']['en']['English']19950.0Lisa Boyle, Kena Land, Zaneta Polard, Don Yanan, Debra K. Beatty, Mark Sikes, Robert J. Ferrelli, Ellyn Dawn Humphreys, Ron Jeremy, Ben RamseyAaron Osborne0
45436030840enYet another version of the classic epic, with enough variation to make it interesting. The story is the same, but some of the characters are quite different from the usual, in particular Uma Thurman's very special maid Marian. The photography is also great, giving the story a somewhat darker tone.5.6837531991-05-130104.0ReleasedNaNRobin Hood5.726.0NaNNaNNaNNaNWestdeutscher Rundfunk (WDR), Working Title Films, 20th Century Fox Television, CanWest Global Communications7025, 10163, 16323, 38978['Drama', 'Action', 'Romance'][18, 28, 10749]['CA', 'DE', 'GB', 'US']['Canada', 'Germany', 'United Kingdom', 'United States of America']['en']['English']19910.0Patrick Bergin, Uma Thurman, David Morrissey, Jürgen Prochnow, Jeroen KrabbéJohn Irvin0
454370439050faRising and falling between a man and woman.0.072051NaN090.0ReleasedRising and falling between a man and womanSubdue4.01.0NaNNaNNaNNaNNaNNaN['Drama', 'Family'][18, 10751]['IR']['Iran']['fa']['فارسی']19690.0Leila Hatami, Kourosh Tahami, Elham KordaHamid Nematollah0
454380111109tlAn artist struggles to finish his work while a storyline about a cult plays in his head.0.1782412011-11-170360.0ReleasedNaNCentury of Birthing9.03.0NaNNaNNaNNaNSine Olivia19653['Drama'][18]['PH']['Philippines']['tl']['']20110.0Angel Aquino, Perry Dizon, Hazel Orencio, Joel Torre, Bart Guingona, Soliman Cruz , Roeder, Angeli Bayani, Dante Perez, Betty Uy-Regala, ModestaLav Diaz0
45439067758enWhen one of her hits goes wrong, a professional assassin ends up with a suitcase full of a million dollars belonging to a mob boss ...0.9030072003-08-01090.0ReleasedA deadly game of wits.Betrayal3.86.0NaNNaNNaNNaNAmerican World Pictures6165['Action', 'Drama', 'Thriller'][28, 18, 53]['US']['United States of America']['en']['English']20030.0Erika Eleniak, Adam Baldwin, Julie du Page, James Remar, Damian Chapa, Louis Mandylor, Tom Wright, Jeremy Lelliott, James Quattrochi, Jason Widener, Joe Sabatino, Kiko Ellsworth, Don Swayze, Peter Dobson, Darrell DubovskyMark L. Lester0
454400227506enIn a small town live two brothers, one a minister and the other one a hunchback painter of the chapel who lives with his wife. One dreadful and stormy night, a stranger knocks at the door asking for shelter. The stranger talks about all the good things of the earthly life the minister is missing because of his puritanical faith. The minister comes to accept the stranger's viewpoint but it is others who will pay the consequences because the minister will discover the human pleasures thanks to, ehem, his sister- in -law… The tormented minister and his cuckolded brother will die in a strange accident in the chapel and later an infant will be born from the minister's adulterous relationship.0.0035031917-10-21087.0ReleasedNaNSatan Triumphant0.00.0NaNNaNNaNNaNYermoliev88753[][]['RU']['Russia'][][]19170.0Iwan Mosschuchin, Nathalie Lissenko, Pavel Pavlov, Aleksandr Chabrov, Vera OrlovaYakov Protazanov0
454410461257en50 years after decriminalisation of homosexuality in the UK, director Daisy Asquith mines the jewels of the BFI archive to take us into the relationships, desires, fears and expressions of gay men and women in the 20th century.0.1630152017-06-09075.0ReleasedNaNQueerama0.00.0NaNNaNNaNNaNNaNNaN[][]['GB']['United Kingdom']['en']['English']20170.0NaNDaisy Asquith0